Tag Archive for: competitive intelligence

Your 10 Favorite Competitive Intelligence Podcasts: B2B Market Research podcast

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What have I learned after recording 100 competitive intelligence podcasts? A lot. Here are the top ten B2B Market Research podcasts and the things I learned behind the scenes.

In this podcast I cover:

  • What I’ve learned from producing 100 podcast episodes.
  • The top 10 posts as defined by Google Analytics and social sharing data.
  • Which topics resonated most with you and why.

Thank you for listening to this episode! If you enjoyed it, please feel free to share it using the social media buttons on this page.

We would also be VERY grateful if you could rate, review, or subscribe to the B2B Market Research podcast on iTunes, Stitcher, or TuneIn.

top-ten

Speakers:

Sean Campbell – CEO of Cascade Insights

[Modified Transcript]

After reaching 100 podcast episodes, what have I learned? That’s the subject of today’s podcast.

This podcast is brought to you by Cascade Insights. Cascade Insights specializes in B2B market research services for B2B technology companies. Our specialization helps us deliver detailed insights that generalist firms simply can’t match.

To learn more about us, visit our company profile, check out our free competitive intelligence resources and sign up for our monthly newsletter.

Back in January 2012, I launched the Competitive Intel podcast. Hence, I’ve been producing this podcast for over three years now, which has led to 100 episodes.

Given that, I thought it was the right time to reflect on some of the most popular episodes (based on Cascade Insights’ Google Analytics statistics), and even talk a little bit about what it’s been like to produce a podcast.

With that, let’s get into the top 10.

The top 10 competitive intelligence podcasts

1. Our most popular podcast is the one I did focused on the 5 essential truths of CI analysis.

I based that podcast in large part on an article that a CIA analyst had produced, reflecting on his 40 years in the intelligence business. I talked about each one of the truths he mentioned in turn. And the one that I want to return to is his third truth, “good analysis makes the complex comprehensible, which is not the same thing as simple.” That is a really, really good point to remember. Again, if you want to see the five in total you can always go back and revisit the page where we’ve got that podcast hosted.

2. Our next most popular podcast is the one I did focused on the needs of product managers.

Product managers have questions that run deep, especially in the B-to-B tech sector. I covered a few of these in the podcast, 15 Competitive Intelligence Questions Product Managers Need to Answer.

In the podcast, I looked at a few things — for example, what kind of assets drove customers to pare down the list of vendors they actually engage with? This is a sign of when you’re perhaps losing early in the process and not even aware of it.

I also went into things like what personas were involved in the buying process and what roles were involved. Additionally, I discussed other factors, including the cost of the solution and the key buying criteria that drove the customer to consider a competitor’s offer.

3. The third most popular competitive intelligence podcast was the one that I did on the changing nature of the B to B sales and marketing process. I interviewed the author of The Challenger Sale for this, and talked a lot about how competitive intelligence must adapt to these changes. This remains one of the more popular podcasts in terms of actual social shares, even though it’s third on the list in terms of actual page views.

4. The next most popular podcast in the fourth position is the interview with Mark Smith on NodeXL. The NodeXL podcast is popular because NodeXL is of the best tools you can find that provide a “quick” introduction to social network analysis. That’s a funny way to put it, because I don’t think it’s really a quick way to do social network analysis. But NodeXL is the tool gets the closest to it, given it’s just embedded right in Excel.

5. The next most popular podcast is when I talked to Kris Wheaton from MercyHurst. MercyHurst is a great institution, one of the best ones out there when it comes to “growing” competitive intelligence analysts. I had a really interesting discussion with Kris about what makes a good intelligence analyst and the program at MercyHurst overall. That podcast was obviously of interest to a lot of folks.

6. The next most popular competitive intelligence podcast is when I talked about hiring world class analysts. In the podcast I talked about a lot of things that go beyond just pure analytical horsepower, down to the level of, “What’s the kind of person you’re hiring, and can they be a really effective consultant?” This, I believe, is key when you’re building a shared services team like a CI team.

7. The seventh most popular podcast was my interview with Wayne Jones of IBM. I think what probably struck people about this podcast is when Wayne talked about the research agenda and how important it is to establish a really good one if you’re going to build a well performing CI team.

8. The eight most popular podcast was on uniting competitive intelligence and market research efforts. I think the reason this podcast is so popular, is people have struggled to figure out the boundary between these two disciplines. The outer edges of each discipline is pretty easy to identify, but the boundaries between them as they merge into one another…well…that’s a little harder. I talked about marrying the customer-centric view of market research with the outward view of competitive intelligence — and I can only assume that that was one of the key things that resonated.

9. The ninth most popular podcast in terms of page views — although interestingly enough, this is one of the more popular ones in terms of actual podcast audio plays — is Three Key Ways that Competitive Intelligence is Different Than Spying. I talked a lot about how competitive intelligence professionals disclose their identity, how they practice their craft, and walked through a lot of the differences.

I think this is a question that many people have about competitive intelligence, including where the line is in terms of ethical and non-ethical behavior.

10. The tenth most popular podcast, 5 Fundamental Truths, distills key takeaways from the CIA analyst’s article I discussed at the top of this post.

What I’ve learned about podcasting…

In closing, I’ve also learned a lot of things about making a podcast. Audio quality is increasingly important. It’ll never be perfect, especially when you’re doing interviews — particularly if those folks are remote and in other countries –just because audio quality issues aren’t fully within your control. But I constantly strive to make those things as best as I can.

I’ve also learned that you like transcripts just as much as you like the podcasts, and I think there’s a simple reason for that. For some people, I think sometimes it’s easier to read it than listen to it. Hence, I’ve made transcripts available and will continue to make them available for our archived podcasts.

What’s the road map for the next 100? Well, maybe some better intro music and perhaps closing music. But if I actually get to that, I’ll probably have too much free time on my hands. :)

Then I’ll probably do some more interviews with luminaries in the CI space, and will also talk about how various disciplines interact with the world of competitive intelligence.

With that, I want to thank you for listening to this podcast.

Photo thanks to: iabusa

How Predictive Analytics Turn Data into Insights – Interview with Author Thomas Miller

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Today, we’re featuring a conversation Sean Campbell, Cascade Insights’ CEO, recently had with Thomas Miller. Thomas is a faculty member at Northwestern University and has written a really fascinating book on predictive data analytics called Web and Network Data Science: Modeling Techniques in Predictive Analytics.

web-and-network-data-science

Sean:

So my first question for you, Tom, is if you would share with us a little bit about the book and also about yourself.

Tom: Certainly. Well, the book that Sean’s referring to is my web and network data science book, which was published at the end of last year, 2014. It covers two areas: web analytics and social network analysis (referred to as network science), and tries to bring them together to show the overlap. There is, of course, a section in there and it’s kind of a mutual admiration society. We refer to Sean’s book on Going Beyond Google as well.

What we try to do in the web and network data science book, which is intended for a course we do at Northwestern, is provide students with the right kind of skills and overview that they need to gather information from this humongous resource, the world wide web.

There’s just so much out there, and it’s hard to know how to pursue it, and you need to have the skills to do it. It’s not just point and click. You also have to have some computer programming skills to do it. (Well, to do it most efficiently.) That’s what the book is oriented toward — to provide that overview — and then we use it in the course. The update of the course will have the same name, Web and Network Data Science.

I’m also involved in independent consulting. I’m a kind of entrepreneur myself; I have a small company that is pretty much in startup mode right now, oriented around data science.

Sean:

Excellent. I think the book you’ve put together is a great resource.  I want to ask you a question that ties into your comment about this propensity among a certain type of individual. I’m not trying to be pejorative here, but I’ve met this kind of individual at conferences and in companies we’ve worked with over and over again. This person is fond of saying something like, “Well you know, Internet data is just data. You can’t derive any real meaning from it. It’s just information.”

Do you run into these types of folks? I imagine you do.

Tom: Well, it could be a matter of people being used to what was done in the past where they were doing primary research, design research, and custom research: they collected data for the purposes of a study. When we’re talking about the internet, we’re talking about this massive secondary data resource where you don’t have to design the study in advance. You’re using data that are already available.

That’s part of it, I think, is this education that’s needed as far as the value of secondary research generally and secondary information sources.

I think the other part of it is a lack of understanding of unstructured or semi-structured text, and what you can learn from that.

I think the other part of it is a lack of understanding of unstructured or semi-structured text, and what you can learn from that. In traditional research, statistics courses that you may have had in your undergraduate days (and many people have had them in one form or another), they deal with spreadsheet-like data where it was in columns and traditional databases and relational databases, and they don’t do a lot with text.

The web is text. It’s mostly text. You have to dig into that, and you have to extract. You have to scrape.

The web is text. It’s mostly text. You have to dig into that, and you have to extract. You have to scrape. You have to do the kinds of things that we do in network data science to get some meaning out of it. You have texts, which are unstructured. Somehow you translate those into numbers, which are analyzable in your models. That’s a challenge, and people are not very well educated in that area.

Sean:

Those are all really good points. Particularly the point about quantitative analysis vs. text analysis. The Internet is so text heavy, and people sometimes don’t have the right tools to, in essence, turn that into something that’s a little more quantitative for them.

Could you describe the tools that you think are critical to this type of analysis? For example, I know you mention Python quite a bit in the book.

Tom:

Python is used in the book, and it’s used extensively in data science. Python is primary. That’s because it is such a wonderful language for data munging and data preparation, sometimes called parsing of data. It’s a well-structured language — you don’t have a compile cycle, so you can do things more quickly.

Python is primary. That’s because it is such a wonderful language for data munging and data preparation, sometimes called parsing of data. It’s a well-structured language — you don’t have a compile cycle, so you can do things more quickly.

Prototyping is a lot faster in Python than in other languages, and it’s become the de facto standard you might say for systems programming work. It has replaced Pearl in many contexts. Because of that, it’s a good initial language to learn. With Python, there’s also the possibility of easily working with databases.

When we talk about databases in this context, we’re not talking about relational databases that you might look at in, for example, an accounting environment. In financial accounting, you have all these dollar signs and numbers to keep track of in rows and columns. What we’re talking about with text is we have to have an unstructured format or at least a more flexible structure. That’s either XML, which is markup language, or JSON — and JSON in the web world is primary these days because you can actually read it and read it with ease.

You have the general purpose language for manipulating text, for parsing: that’s Python. You have packages within that world of Python to extract data, so you can scrape (or crawl as it’s sometimes called) spidering the web to gather data.

Then you have to scrape the web pages because they’re marked up with HTML (which is structured like text) and you have to get rid of all those extra tags and codes and gather the data that you want — often from within the paragraph tag. That requires a good Python package to do.

Once you have the text, and it’s organized into different regions, you have to have a place to put that text. Initially, that place could be XML to allow things like the JSON format, which is JavaScript Object Notation, in the database format.

With JSON, it’s readable and you’re matching up the codes. If you had, say, an email message, you have the “from” node, the “to” node, the cc and the bcc, and then you have the subject and you have the body and you have the web identifier. All of those are keys that can then link to the values associated with them. Some of those values are individual values like the from-node, or an entire array of values, like the to-node and the bcc and the cc.

All of that can go into this semi-structured, you might say, format called JSON. Then that’s that last file you have to understand, to understand the file structure, JSON. Now you’ve got it in JSON and now you want to put it in a database so you can actually do queries on it, and there are a lot of ways of doing that. In the databases, there are specialized databases like MongoDB, but also some other tools, for example, PostgreSQL where you can bring JSON in and work with that semi-structured text and JSON, within a traditional relational database as a JSON extension. You’ve got all that.

Now you have the other issue of querying the data. Well, how do you query text data? You’ve got to query text data with flexible tools.

The traditional database tools are not sufficient because they require a look for a specific value, when in text, you can say something in many ways and often.

The traditional database tools are not sufficient because they require a look for a specific value, when in text, you can say something in many ways and often. And if they were taking email messages, people are typing. Well, people make mistakes when they type, so how do you do a search across that? In that regard, you need to have an actual language-based tool, or, say, a tool like elastic search that’ll do an effective search across the email messages that you’re looking for.

All of those are kind of standard toolbox utilities that you need to have to work effectively in the domain of the web.

Sean:

So how technical do you need to be to mine these type of assets? By that, I mean whether you’re talking about social network analysis or mining communities or web traffic statistics — or anything else for that matter — related to the subject at hand.

Because my experience has been it depends on the data source you’re looking at, as well as how structured that data is and whether the target website gives you an API or gives at least decent search capabilities. Hence, I know it’s not an easy thing to answer “Yes, you need to be technical” or “No, you don’t need to be technical.” As it obviously seems to depend a lot on the data source and the questions you’re asking.

Tom:

Yes. I think many of the studies we’re talking about are team efforts, so first there’s a modeler. The modeler doesn’t always understand the IT part, the database part.

There’s the person who does the parsing to put the data into the database, and that person may not understand databases. We have forty faculty and all the skills we could imagine, but if I were to query one of those faculty members who may be teaching, say, the machine learning course and ask, “Could you take on a section of the database course this term?” often I get resistance: “I’m not a database person.”

It’s hard to know it all, and what you need to do is if you’re an independent consultant is outsource. You bring in the other expertise that you need because it’s not only hard to know it all, it’s also changing. Every year there’s some new technology that’s out there, the latest and greatest. One person can be aware of what’s out there, yes, but one person can’t be a specialist, an expert, in every one of those areas. It’s just too much.

One person can be aware of what’s out there, yes, but one person can’t be a specialist, an expert, in every one of those areas. It’s just too much.

As far as what you can do easily: yes, there are companies that provide APIs, easy access and search facilities, so you don’t have to know anything technical. You just have to type in the string. Think of all the intelligence that’s behind Google itself and the search engines that are out there. Bing and Google, they have all kinds of algorithms and intelligence underneath them to give you the results, but then anyone can get those results. Any of us can type that in. It’s the people with the most technical skills that have the ability to dig deeper and be more efficient in their searches.

How much do you have to know to do basic stuff? Maybe next to nothing as far as specialized skills. But if you want to do a really detailed study, often you’re going to need to know more. This division between the IT and modelers is a real one. We see it all the time in the field of data science, and some people come to data science from IT and some people come to data science from statistics and from modeling. Often, they don’t speak to one another. Often, they’re speaking different languages as far as computers. It’s a challenge, but both are needed to do the work.

Think about the network science part itself, where you’re looking at links and followers and following a Twitter chain or a Facebook chain, or a network on LinkedIn. To analyze those requires special skills in network data modeling. Their IT folks don’t have that, usually. A lot of the modelers don’t have that. Network scientists would have that.

Sean:

That’s the interesting aspect, right? It’s like all new types of analysis efforts borrow a little bit from other disciplines but then are also inherently unique unto themselves.

So with that in mind, let’s address a few more areas. One is social network analysis. I think if you went to an average conference, discussion, or whatever that was on mining internet data, another logical sub-bullet to that would be, “Well, how do you do social network analysis?”

This is an area that the second you crack it open you start running into things like, “betweenness centrality,” and other things like that. Hence, I think some people quickly feel that social network analysis, in any meaningful way, is beyond them.

Given that, what are some of the baby steps people can take to at least become more aware of the space or the tool set? I mean, you’re educating students all the time, so when you start talking about social network analysis, how do you get them started in a way that they feel like they’re doing something meaningful in a fairly quick manner?

Tom:

Well, to do something meaningful, you’re going to have to employ some sort of program. There is a variety of programs out there, with some easier to use than others. I look at open source; I look at programs that are used for other purposes as well, primarily. In that regard, there’s a Python package called NetworkX. In R there’s iGraph, and some specialized packages on top of that deal with social network analysis.

You start there and you start with examples that are already completed. I can talk about email a little bit. In the book, I show Enron. We do an analysis of Enron and we use R in that particular analysis. Students in my class learn, first of all, how to produce the example. Then I ask them to take it another step further, to do something new, to explore it more extensively.

Now, there are some nice packages out there. One in particular, Gephi, is interactive. It’s easy to use so you can just point and click a graphical user interface. You don’t have to know a lot of programming to use it, so one way to explore, to learn initially, is to export the data from Python into a format that this Gephi program can understand. Then you bring the data into Gephi and you point and click your way to a new analysis.

…so a network of 300 people, can get really complicated, dense and hard to understand unless you can dig into it and find your way to the most important players.

The big challenge in networking is finding the interesting subnets. That’s been a real challenge. Even a network of 300 nodes or people, so a network of 300 people, can get really complicated, dense and hard to understand unless you can dig into it and find your way to the most important players. That’s what you were talking about when you mentioned betweenness centrality. Betweenness centrality, eigenvector centrality — these ideas are in some ways related to what we understand about Google and the links in Google, and references and prestige and “who knows what” and “who’s getting referred to.” All of that is related.

These are just ways of finding our way to the influential people. It might be people with power, it might be people who know more. It might be effectiveness, but it’s people who are referred to more. That’s a tool. The statistics relating to the people or the nodes of the network are ways of getting to the more important ones. When you identify the more important ones, then you have subnetworks you can work with.

One approach is what’s called an egocentric network. You can reduce the problem down to a smaller problem where you’re looking at one person, so it’s your network, Sean, and the people that you know. Then maybe one step beyond that, one order beyond that. You look at all the links that you have of the people you know and then look over one more level to the people they know. As that network grows, maybe not all the way because you’re going to mess with a few orders or magnitude of length, you’ve got a very large network to deal with. That’s one way of approaching it. So have a focus.

We have a project in one of our classes, the data prep class, where we’re looking at the Enron email data set, which is public domain, and the students are asked to see if they can find the culprits. Where were the problems? Where did they originate? To whom were these people talking? You’re looking for connections. You’re digging for problems in accounting. Or digging for problems with the California blackouts and brownouts that were occurring at that time that were tied into Enron. The students dig in the text to find the people who were talking about those things, then they find to whom those people were talking, and it’s a lot of fun. It’s detective work. Social networks are used that way.

I think one of the reasons social networks are being talked a lot about is because marketers see them as a way of finding people, finding new buyers. If you buy an iPhone, your friends might more likely buy iPhones. It’s a higher hit rate in terms of the marketing effort to go to the friends of the friends of people who are current customers. There’s not a lot of, I think, general interest in the theory of networks so much as finding a way to new customers. That’s what I think is the driving force behind that interest.

Sean:

I completely agree. I think that, especially in a business-to-business context, that’s a lot of where this is coming from and it’s a lot of where the commercial tool set obviously is pointing at this point.

One last broad question around predictive analytics: I think one of the things that intrigue people about this, beyond the examples you mentioned which are critically important, is also the fact that we can peer into the future using these types of approaches.

The example I give all the time is the one around search traffic statistics. If you had a competitor that was getting a three x increase in search to their website, specifically if they were selling a SaaS product where customers can just buy without talking to the sales team, that traffic increase is a clear indicator that there are going to be future sales for that company. Meaning that some of that web traffic will translate into a future sales number.

Hence, in many ways, web traffic analysis can be predictive because the sale cycle might last six to twelve months or twelve to eighteen months, so when you look at today’s statistics, you’re seeing a little bit of a view into the future – in terms of next year’s sales for the target company.

With that straightforward example in mind, what are some of the more interesting things you’ve seen when it comes to doing predictive analytics?

Tom:

Yes to all of what you just said. Predictive analytics is a term I look at as synonymous with data science. What we’re doing is we’re taking a business problem; understanding of a problem, and the business, taking data and IT and we’re creating models and putting them together. Ultimately, what we have to do is speak to the business problem. We have to solve that problem. A lot of times that problem is, “What are we going to do about sales,” or, “What are we going to do about our competitors and how are we going to increase sales?”

The tools that we’re talking about in that area are largely traditional statistical models or machine learning models that do essentially the same thing in a more flexible way. We have explanatory variables. We have things that we understand right now. You mentioned one of them which might be search engine performance, however measured, and we have the response. Response variables are what we’re trying to predict, which is, say, sales, sales in the next quarter.

We look at the path, and we see how those explanatory variables related to the response, and from that, we build a model. The models that we build are sometimes regression models when we’re trying to predict sales or quantitative response. Other times, we’re trying to build toward a classification model or we’re trying to predict the group. It might be buy or not buy, it might be pay off the loan or not pay off the loan, some kind of categorical response, often binary. Which brand a person’s going to purchase, that’s a categorical and multinomial response.

We build models that are classification models in that context. All of these things are well understood and there are many methods to deal with these problems, and that’s essentially what we do. I have another book which just came out this month, Marketing Data Science, that shows how to do that chapter by chapter. There’s a business problem in every chapter and it shows what kind of model could be used.

Even traditional models can be used. You don’t have to choose the machine learning algorithms to get something useful. You can make predictions about “what if,” types of questions. For example, what if we reduced the price of this product by ten percent? What could we expect in terms of sales response given the competitive environment? You play those kinds of simulation games within the context of the model. They’re exciting, interesting, and very important applications of predictive analytics.

Sean:

Those are all excellent examples. Well, I think we could keep talking for a while because we’re both pretty interested and invested in this space, but I’m going to have to wrap it for now. I want to thank you for joining me for this intriguing conversation!

Tom: Most welcome, Sean. I enjoyed it!

Lost a Client? Don’t Generalize from a Specific: B2B Market Research Podcast

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Episode #100 of the B2B Market Research Podcast: Lost a Client? Don’t Generalize from a Specific

During this podcast, we cover:

  • How to determine if losing a client is an isolated incident or if you have a new competitor.
  • Why it’s important to enlist your sales team’s help.
  • How to build a digital footprint of the competitor by investigating its size, workforce, partnerships, funding and other telling metrics.

Thank you for listening to this episode! If you enjoyed it, please feel free to share it using the social media buttons on this page.

We would also be VERY grateful if you could rate, review, or subscribe to the B2B Market Research podcast on iTunes, Stitcher, or TuneIn.

Upset problem child with head in hands sitting on staircase conc

Speaker:

Sean Campbell – CEO of Cascade Insights

[Modified Transcript]

How do you know that you’re not generalizing from a specific? That’s the topic of today’s podcast.

This podcast is brought to you by Cascade Insights. Cascade Insights specializes in market research services for B2B technology companies. Our laser-sharp focus means we deliver detailed insights that generalist firms simply can’t match. To learn more about us, visit our company profile. Also, please check out our free competitive intelligence resources and sign up for our monthly newsletter.

Now let’s get into the topic of today’s podcast. Regardless of your role in your company, you’ve probably come across this scenario: your phone rings and there’s a loss to a major competitor. Worse yet, the person calling about the loss is an executive.

In this scenario, it is important to distinguish whether a new competitor has been born or if you’re on the verge of generalizing from a specific. That is, wasting time analyzing a loss that’s highly unlikely to happen again.

So how can you tell the difference? How do you know if you’re generalizing from a specific or not? How do you know if you’ve just lost in a meaningful way? These are some of the topics that we’re going to cover in this podcast.

Here are some things to consider:

How often has this occurred?

It’s such a simple and elemental question, but it’s a hard one to ask, especially if an executive is asking you to investigate the problem. But it’s an important question to ask, regardless. No matter how big the deal was, no matter how important the client was, if it’s only happened once, it may not happen again for a very long time. That’s the first thing you need to consider.

Have you heard of this competitor before?

The next question you need to ask yourself is whether you have heard of this competitor before? Do you have any additional metrics on their growth? Do you have any history on the competitor?

One of the easiest things you can do is to start building a bit of a digital footprint on the competitor. Do they generate a signal? Do they have any meaningful traffic to their site? Do they seem to show any meaningful customer engagement via case studies or anything similar? What’s the makeup of the competitor’s leadership team? Does this leadership team appear to have the right amount of market insight and ability?

Has your sales team heard about the competitor?

Develop a tracker that your sales team fills out whenever they hear about a competitor for the very first time.

One of the things that you can do – and you can even do it somewhat programmatically – is to develop a tracker that your sales team fills out whenever they hear about a competitor for the very first time. It doesn’t even have to be a competitor that they lost a deal to per se. They simply use the tracker for the first time they heard a new competitor’s name come up in a sales conversation.

Given the fact that most B2B sales cycles take somewhere between 12-18 months to run, sometimes more, sometimes a little less, this provides a great set of early warning signals that you’re collecting over time.  So in sum, make sure that you talk to sales team members and say, “Have you heard of this competitor before?” Maybe you haven’t lost to the competitor more than that one time, but the sales team has heard about the competitor several times across a number of different deals over the last few months or the past year.  And that’s news you’ll want to have on hand.

Don’t be afraid to call the requestor’s bluff

This can be difficult to do, but it’s important. You need to ask, “How important is this research effort versus other ones that you might undertake?” That’s an important consideration, especially if all the other signals seem to show that this was an isolated loss.

Unknown company? Dig into the competitor’s background and biographies

Finally, what can you do when the competitor is small and somewhat unheard of? Well, first, you want to look at the biography of the owners. Small companies tend to be driven by the talents and the abilities of the leadership of the company and that’s an important thing to investigate. You want to look at the background of these individuals, to determine what kind of companies have they run in the past, and what kind of abilities do they bring to bear today.

You also want to look at simple things like metrics on company size. You can dig into this with tools like LinkedIn or even PrivCo. If the company seems to have received a certain amount of funding, that would be another thing to look at. In short, there are a number of databases you can use.

You also want to make sure that you look at the development staff of the company, because in many cases, especially with technology companies, the number of executives and managers and people that they have on staff may not necessarily be as visible in the same way that the development staff is.

For example, the development staff may be offshore. So, if you simply do a profile of how many employees they have on LinkedIn, you may not be able to see the entire size of the development staff. But you can dig into this. It just takes a little bit more time.

Also, does the company have any meaningful partnerships? Maybe the company has been able to expand their reach through partnerships and the level of competition they’ve been able to generate.

Make sure you look for observable customers, not just case studies.

Make sure you look for observable customers, not just case studies. You can mine things like LinkedIn, job postings, and a variety of other databases to get a sense of whether this organization has sold to a limited number of customers (of which this one loss is included,) or whether they have a certain amount of momentum and you’re about to hear a lot more about them.

Interview your competitor’s customers

…At a minimum you can at least attempt to interview the customer that you lost.

Finally, don’t stop until you’ve interviewed at least one, and preferably a number, of their customers. Again, while you don’t want to generalize too much from a specific, at a minimum you can at least attempt to interview the customer that you lost. If this is a small competitor where this is one of their first wins, and your sales team is saying that they’ve heard a lot about them, you might gain a lot of insight even from that single interview. Again, don’t over-generalize from that specific conversation, but still continue to be diligent until you actually have that conversation.

So in sum there are a bunch of key questions you want to ask yourself when the phone rings and you’ve heard about a loss for the very first time. How often has this occurred? Have you heard of this competitor before? What do other sales team members know about this potential new competitor? Don’t be afraid to call the requestor’s bluff. Finally, use some of those techniques I just mentioned when the competitor is small and somewhat unheard of.

Thanks for listening, and don’t forget to check out our free resources and sign up for our monthly newsletter.

Predicting Competitor Growth – 3 Tools for Analyzing the Buyer’s Journey: B2B Market Research podcast

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Episode #99 of the B2B Market Research Podcast – Predicting Competitor Growth – 3 Tools for Analyzing the Buyer’s Journey.

We cover:

  • Competitive intelligence tools for analyzing relative search engine rankings and web traffic data.
  • How the B2B buyer’s journey is rapidly changing.
  • Why it is imperative to leverage the predictive capabilities of competitive intelligence tools.

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Speaker:

Sean Campbell – CEO of Cascade Insights

[Modified Transcript]

In this episode we’re going to talk about a few different tools that you can use to predict competitor growth and in essence analyze the front-end of the B2B buyers’ journey.

This podcast is brought to you by Cascade Insights. Cascade Insights specializes in competitive intelligence services for B2B technology companies. Our specialization helps us to deliver detailed competitive intelligence insights that generalist firms simply can’t match. To learn more about us, visit our company profile. Also, don’t forget to check out our free resources and sign up for our newsletter. With that, let’s get into the topic of today’s podcast.

I recently gave a presentation at the SCIP International Conference in Atlanta, where I shared a number of different tools that organizations and competitive intelligence professionals can use to analyze the B2B buyer’s journey.

I also discussed how important it is to understand how the buyer’s journey is changing. In fact, it’s changing dramatically in terms of how customers buy. I’ll be addressing this as well in today’s podcast.

Competitive Intelligence Analytics Tools: Search Ranking & Web Traffic

With competitive intelligence tools, you can analyze metrics such as the search position and traffic volume of competitors’ websites. These insights truly matter. In fact, the CI analytical tools I’ll be discussing are even somewhat predictive.

For example, if traffic is increasing to a competitor’s website that’s 5x your traffic, and the sales cycle is six to 12 months or 12 to 18 months, you can almost guarantee that there’s going to be an increase in sales for that competitor at some point down the line. So the tools I’ll be talking about here are, in many ways, simply predictors of next fiscal year’s success for your competitor — even more so than they are focused on this year’s performance.

Why Positioning for Long Tail Keyphrases Is Important

Positionly is a great competitive intelligence tool for analyzing relative search rankings, and not only because it’s relatively inexpensive (we’re talking about plans that start at five bucks a month). Positionly excels in clearly showing where competitors rank –  or position — in a given search engine.

So what do I mean by that?

Let’s take an example from the data analytics and data visualization space. While there’s a bunch of different players in this space, for a concrete example I’ll use Tableau, Qlik, and Birst.

Assume you wanted to know “Where do all these companies rank when a user types the phrase ‘visualization software’ or ‘data visualization software’ in Google?” or more specifically, “Where does Tableau show up? How many links down are they? Are they the second link, the third link, or the 33rd link (meaning they’re on page three in Google’s search results.)?”

Positionly makes this very easy to do.

First, you’d simply list the competitor web domains you want to track, then specify the keywords you want to track. Positionly will immediately tell you that Tableau is going to position as the third link in the search results for “visualization software,” the 12th link for “data visualization,” and the 15th for “business dashboard tools.”

Meanwhile, a competitor of theirs, Qlik, is going to rank as the 29th link for “business dashboard tools” versus Tableau’s 15th position. It’ll be at a distant 47th (fourth page) ranking for “visualization software,” and for “data virtualization” it won’t even be listed in the top hundred search result links, compared to Tableau’s 12th ranking.

These are essential competitive intelligence insights, especially given that B2B buyers are increasingly using web search at the start of their purchase decisions. Today, the marketing funnel is really the front end of the sales cycle, as people filter out the vendors they’ll select and the organizations they’re going to have deeper conversations with beginning with a web-based search.

It’s crucial to leverage a tool that tells you where you rank for those long tail keywords — those problem statements that customers are trying to solve for — relative to your competitors.

Therefore it’s crucial to leverage a tool that tells you where you rank for those long tail keywords — those problem statements that customers are trying to solve for — relative to your competitors.

Using the example of “data virtualization software,” you’d need to determine where you position for that keyphrase versus your competitors, because the problem may have more to do with marketing than sales or product performance. But you won’t have any sense of that unless you put a competitive lens on things with tools like Positionly.

How To Analyze Competitor Web Traffic Volume

Another comparative metric to consider is the relative amount of web traffic that is going to competitors’ sites. For this type of analysis, you can use tools like SimilarWeb.

SimilarWeb does a great job of actually giving you some broad traffic statistics that you can readily graph. You can put two competitors next to each other in the tool (for free on the basic site), and it will show you statistics and traffic between, let’s say, Tableau.com and Qlik.com. So if you were to run that report, it would make it pretty obvious that Tableau is getting more traffic.

An important thing to keep in mind is that having the right taxonomy and the right domains matter. For example, if you were to analyze Tableau.com with SimilarWeb, it would seem as if they were getting no traffic roughly about a year or a year and a half ago, but if you were to put in tableausoftware.com it would look like that all of their traffic had effectively started about a year and a half ago.

All you’re really seeing there is a change in the domain name (from tableausoftware.com to Tableau.com) in terms of where the bulk of their marketing efforts and web traffic were going. They obviously were able to get the shorter domain name — Tableau.com — and they directed users there. This is a good example of why you need to make sure that you’re looking at the results holistically.

Another feature of SimilarWeb (given the product’s a little more expensive than Positionly) is that you can use their widget tool to graph three competitors at the same time. That is available for free, but obviously you’ll receive a lot more competitive intelligence capabilities if you purchase the product.

Another tool to consider is Follow.net. Follow.net is much less expensive than SimilarWeb. And while it’s a little more expensive than Positionly’s base plan, Follow.net will actually pull in traffic reports from places like Compete.com and Alexa which allows you a slightly more comprehensive view of the competitive landscape.

Follow.net will also give you some information on what keywords were driving traffic to your competitors’ sites, which is something you would want to review to see what problem statements (long tail keywords) B2B buyers are using in their search queries, and then who is getting that traffic. You can even access some display advertising data, such as what kind of ads competitors’ are running and related intel.

If you’re not analyzing where you position versus your competitors, and determining the volume and type of traffic they’re generating, then you’re missing out on statistics that in many ways can be very predictive.

In closing, if you’re not using any of these tools (or tools like these) as a competitive intelligence professional, you’re missing out on what is essentially the front end of the sales cycle today. And we all know this to be true. We know that almost everything we buy these days (whether B2B or B2C) starts with some type of web search.

Hence if you’re not analyzing where you position versus your competitors, and determining the volume and type of traffic they’re generating, then you’re missing out on statistics that in many ways can be very predictive. And given prediction is the hallmark of an excellent competitive intelligence professional, you don’t want to miss out on that.

Thanks for listening, and again, don’t forget to check out the free resources available to you and subscribe to our newsletter.

The “Competitive Intel” Episode 29 Transcript – Thinking about Apple: Don’t Generalize from a Specific

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Business people commonly discuss how to emulate Apple’s success. Let us be clear, there’s probably little that many businesses can learn from Apple. In point of fact, both of Apple’s wildest successes have held the seeds of their own undoing. Just as Apple’s success at the dawn of the personal computer era was eclipsed by Windows PCs, the iPhone and iPad are being overtaken by Android devices.

The Reality of Shaping New Market Segments

Apple’s strategy has always included end-to-end control over the ecosystem, as opposed to the multi-vendor approach associated with Windows PCs or Android devices. With the iPhone and iPad, that model made Apple enormous amounts of money and redefined the industry. How did they do it? In short, they got lucky. They seized a once-in-a-decade product opportunity by modifying an existing product. In other words, they jumped into smartphones by modifying the iPod. If your industry is ripe for a once-in-a-decade innovation, and you already have a product ready-made to build on, then you too can be like Apple.

In opening new potential in the smartphone segment, Apple also set the stage for competitors to benefit, which has contributed to the rise of Android devices.

The Oft-Overlooked Writing on the Wall

Analysts tend to conflate the brilliant user experiences of products such as the iPhone or Tivo with an apparent invulnerability. In reality, the risk of being displaced by alternatives is present for any product, no matter how innovative and unique it may be.

Being a first mover is ultimately a temporary advantage, even though it may generate dramatic success for a time. First movers such as Apple often seek to control the ecosystem to protect the intellectual property of their innovation. That approach leaves them vulnerable to open, multi-vendor alternatives, which have advantages such as economies of scale and customer choice that can create strong competition for market segment share.

Precious Pearls of Wisdom

While a huge portion of Apple’s success is due to luck, there are a few true strategies they employed for a sustained competitive advantage. For example, Apple simplified the supply chain and controlled costs by offering only a small number of iPhone variations, which it manufactures at very high volume. It’s better to buy 12 million of one screen than 1 million of 12 screens.

More importantly, Apple did not let the fear of cannibalization prevent it from bringing a blockbuster product to market. Most companies are unwilling to take this risk. But as Tim Cook said during an earnings call, “If this is cannibalization, it feels pretty good!”

By Sean Campbell
By Scott Swigart

The “Competitive Intel” Episode 28 Transcript – Flyby on Wargaming

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To understand what a competitor is likely to do, put yourself in their shoes. That’s the core idea of wargaming, a framework for setting strategy that dates back at least to Frederick the Great. Using a variety of approaches that range from thought experiments to computer modeling, teams answer the question, “if I had the competitor’s challenges and constraints, how would I go about winning?”

The Role of Wargaming in Your Competitive Intelligence Practice

When you play the role of a competitor, your focus on winning from their point of view helps you avoid underestimating their chances of success. That can be an excellent approach to refine a strategy, and this added perspective can be immensely helpful to validate planning around a product launch or a series of product investments, for example.

Participants should have some experience with other competitive intelligence frameworks, such as Porter’s Five Forces and Four Corners Analysis. More important still, the team should have an intimate understanding of the competitors, in terms of both quantitative measures such as market share and growth, as well as qualitative ones such as corporate culture and motivation.

Structuring a Wargaming Exercise

During the wargaming exercise itself, participants are put in a pressure cooker, and some attention should be paid to the fact that they are being asked to play a “devil’s advocate” role, hypothetically attacking their own real-world interests. The first step in the process actually benefits from that fact, as the team analyzes the landscape and perfects a series of moves your company will take to elicit a reaction from competitors.

In the next stage, participants test the plans they have put in place, attempting to countermand them in the hypothetical role of a competitor, possibly using computer models to put scenarios to the test. Finally, the team goes back and revisits the planning stage, refining the initial plan to better anticipate the responses made by competitors.

What Your Organization Should Take Away from the Experience

The goal of wargaming is not to predict the future. It’s to more fully understand the competition and the options that are open to them in response to actions that your company might take. Like most competitive intelligence activities, it’s about raising your odds of success. In that sense, the primary goal of wargaming is to prepare for more eventualities, in terms of what your competitor might do, looking ahead several moves as one might in a game of chess.

A common and valuable outcome to a wargaming exercise is to say, “I never thought the competitor would do that, but it’s really their most rational course of action.” Doing so brings you a little bit closer to having the oft-mentioned “20/20 hindsight” when it can actually help you—while it’s still actually foresight.

By Sean Campbell
By Scott Swigart

The “Competitive Intel” Episode 27 Transcript – Value Chain Analysis

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It is a basic truism that producing customer value in excess of your cost to provide it creates profit. Michael Porter helps unpack the deeper mechanics of this process with the value chain analysis framework in his book, Competitive Advantage: Creating and Sustaining Superior Performance. In theory, every step of a company’s activity should add value, as a contribution to the larger whole, and analysis of those steps provides insight into profitability.

Main Activities that Comprise the Value Chain

Porter’s framework begins by breaking up the work done by a company (or an industry) into five primary activities, with the premise that value is generated within and between these activities:

  • Inbound logistics refers to the tasks and systems to get raw materials or other inputs from suppliers; value can be added by effectively handling matters such as purchasing, transportation, warehousing, and inventory control.
  • • Operations is the activity to transform those inputs into the finished product, which can be improved through plant layout, production control, automation, etc.
  • Outbound logistics concerns getting the finished product to the customer, with opportunities for efficiencies in areas such as order fulfillment, how the product is stored and distributed, and how waste is controlled.
  • Marketing and sales ensures that customers are aware of your product and helps compel them to buy it; value opportunities include advertising and differentiation within a given market segment.
  • Services enhance the product’s value through offerings such as installation, post-sales support, service plans, training, and so on.

Support Activities Where Innovation Can Bolster Value

Porter suggests that, through innovative management and execution, value can be generated from activities that are—at first glance—merely overhead, or “the cost of doing business.” For example:

  • Procurement activities can help obtain the highest-quality goods possible for the lowest possible cost. 
  • Technology development can improve processes for greater efficiency. 
  • Human resource management can attract, retain, and develop employees to the firm’s overall advantage. 
  • Firm infrastructure improves profitability through legal, accounting, and competitive intelligence strategy. 

The Value-Chain Analysis Framework in Action

Even companies that don’t seem to conform to Porter’s rather manufacturing-centric model, the activities he names can be matched to analogous ones in almost any sector or market segment, to support the following basic analysis approach:

  • Map the generic activities to company-specific ones to identify strengths and weaknesses in each. 
  • Identify potential improvements that could increase the way customer value is generated by each activity. 
  • Evaluate opportunities and form strategies based on plausibility, potential benefit, cost, and risk. 

By Sean Campbell
By Scott Swigart

The “Competitive Intel” Episode 26 Transcript – Benchmarking and Competitive Intelligence

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Effective positioning within a market segment requires an understanding of the relative strengths and weaknesses of your company and its offerings against the competition. While benchmarking is a key capability to fulfill that goal, common misunderstandings about the goals and practices around benchmarking can limit success, even by skilled competitive intelligence organizations.

Refining the Definition and Process of Benchmarking

It’s tempting to reduce the benchmarking process to simply identifying what your product can do that a competitors’ can’t, and vice versa. While those comparisons can be illuminating, you must look deeper than features and capabilities, setting the goal of developing a holistic understanding of an offering’s competitive position. That perspective includes every aspect of a customer’s pre-sale, sale, and post-sale experience.

To keep the breadth of the benchmarking study under control, discipline is needed to avoid making exhaustive comparisons between the products or services under study. Rather than listing differences for their own sake, restrict the focus to “differences that matter” in the marketplace.

Asking Questions about an Offering’s “Job” to Reveal its Competitive Identity

Context based on usage plays an instrumental role in defining the competitive reality that surrounds a product or service. Because customers make purchases to fulfill specific business needs, your benchmarking effort must ask, “what job did the offering enter the market to accomplish?” How does it perform that job, compared to competitors, and are you considering competitors that target that same job (or that may soon do so)?

Moreover, how does the product or service get accessed or consumed, and how well is that characteristic suited to the job the offering seeks to perform? How do customers experience the fulfillment of that job—for example, does the offering meet changing requirements gracefully, or does it place rigid usability demands on the customer?

Expanding Comparisons, Beyond the Product or Service Itself

To analyze the broader identity of offerings, benchmarking must compare the end-to-end experience associated with them. External, operational factors impact the delivery of a product or service, helping to forge customer experience and perception. For example, the sales process can range from a bare-bones transactional model to a high-touch consultative one, with varying degrees of turnaround time, post-sale support, and so on.

Fulfillment factors that include ordering, shipping, and service delivery can also play a large role in establishing how a customer experiences the process. The reputation of support may influence people’s decision to buy in the first place. Sales partners and channels can likewise play a vital role in the decision-making process around a sale, based on aspects such as their size, support capabilities, and geographical breadth.

Bu Sean Campbell
By Scott Swigart

 

The “Competitive Intel” Episode 25 Transcript – Critical Thinking Tools and Tips

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Weighted Ranking Analysis for Competitive Intelligence

A substantial portion of competitive intelligence practice is applying structured methodologies to decision making, both to improve the quality of decisions and to build consensus around those decisions. As introduced in The Thinker’s Toolkit, by Morgan D. Jones, weighted ranking is a powerful set of critical thinking tools that can be applied in this manner. This approach is highly applicable when hard choices need to be made.

Setting the Foundations for a Structured Decision

Deciding among a group of industries that your company could potentially enter is a classic competitive intelligence decision that can be helped by weighted ranking. People generally have little trouble deciding between two options, such as when the eye doctor asks you which of two lenses you see more clearly through; when the list of options to choose among (in whatever context) gets five or ten items long, people tend to get overwhelmed.

In our industry-choosing example, the team should start by establishing a list of the industries to be considered, before identifying which ones are healthy or viable (this latter effort can be handled using frameworks such as Porter’s Five Forces). Next, the conversation can turn to specific reasons the company should or should not enter each industry.

Identifying the Critical Criteria for the Decision

Just as important as identifying a clear list of industries to be considered, it is also vital to methodically identify the criteria for examining each industry as a potential choice. The presence of opportunity is not the same thing as being a good fit, because no company can compete effectively in every industry.

Factors involved in this list of criteria include such considerations as how fractured the industry is, in terms of how many companies presently dominate it, as well as the growth potential of the industry in various markets around the world, in terms of both revenue and employment. Equally important, you must consider whether it is a good match for your company, in terms of your existing expertise, the products involved, sales channels used, capital required, etc.

Applying the Weighted Ranking for a Objective Result

In our example, once the industries to be considered and the assessment criteria have been established, the elements of the decision process are in place. Next, it is necessary to rank the criteria on terms of importance to the decision and place them in an ordered list on that basis. Doing this is fairly simply. For example, you ask, what’s more important, the projected growth rate of the industry, or the current size. Whichever criteria wins gets a point. You do this for every possible combination of criteria. And then you divide the points a criteria got by the total possible. This results in a weighted rank. And every time we do this, our clients realize that what’s really important – when forced to choose – is not exactly what they would have predicted.

By Sean Campbell
By Scott Swigart

The “Competitive Intel” Episode 24 Transcript – HUMINT in Context: Key Questions to Consider

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Competitive intelligence gathering typically requires going beyond online sources, to interviewing human subjects. Even if it’s well within your comfort zone to contact and get information from a group of strangers, the guidelines discussed here can make the process smoother and more successful.

Contacting Human Intelligence Subjects

To initiate the process of gathering human intelligence, you first need to identify the right people to interview. Be sure to get the widest practical range of perspectives by contacting a variety of sources in your own company, as well as outside market influencers, resellers, your customers, and even competitors’ customers.

To increase participation and establish rapport, consider what motivates various individuals; some may simply want to be heard, while others may respond to personal relationships or hope to benefit from the research itself.

Sources for Human Intelligence Best Practices

As you interact with human intelligence subjects, it is worthwhile to take note of what works and what doesn’t, as an aid to establishing your own style and techniques. Beyond that, it is worthwhile to consider outside influences.

Qualitative research techniques from the Strategic and Competitive Intelligence Professionals  (SCIP) website are a great place to start. Analyzing social networks of potential subjects using tools such as  NodeXL and  Twiangulate also yields a wealth of insight. LinkedIn is a great place to search for people who have the background needed to offer insights. Product and domain experts are also easy to find on Slideshare – with contact information. Finally, consider Brant Houston’s Investigative Reporter’s Handbook for tips from the journalism field.

Addressing Your Biases and Those of Your Interviewees

Early in the human-intelligence-gathering process, define how your own experiences, preferences, and allegiances related to a product, technology, or company could influence your findings. Also be cautious of confirmation bias, where tentative conclusions from completed interviews cause you to guide future interviews to support your hunches.

Interviewing a range of people helps limit the impact of interviewee bias. For example, happy employees see a different reality than disgruntled ones, and people tend to assign responsibility for problems outside their own organizations.

Maintaining Ethical Guidelines and Practices

First, do no harm. That means being careful not to put others (or yourself) at risk, and the first important aspect of that guideline is disclosure: never misrepresent yourself as someone you’re not. Don’t pose as a fake customer. You don’t have to volunteer information, but you do have to be truthful.

By Sean Campbell
By Scott Swigart