Your Competitive Intelligence Deserves an Upgrade

Gathering competitive intelligence serves many purposes. It is a core step in creating a strategic plan. It's a necessary prerequisite when considering a new product development initiative. It's incredibly useful for determining brand positioning. And it can be useful in identifying potential competitive threats.

Broadly, the typical competitive intelligence process involves:

• Identifying your competitive set
• Determining your key areas of interest
• Gathering data from various sources
• Synthesizing the data
• And sharing the results with the team

Historically, most of these steps have been largely manual. Developments in recent years have made it possible to automate and streamline parts of the process while simultaneously providing unique insights that would be hard to do at scale.

Tools like Speciate AI can now make the competitive research process simpler and more effective. Here's how.

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Identifying Competitive Sets

When surfacing competitors, it's common to identify both direct and indirect competition.
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Direct competitors are fairly straightforward. Most organizations are familiar with most of their direct competitors because they've had to meet them in the marketplace. Common practices like doing a win-loss analysis of closed deals and chatting with salespeople about what they're seeing in the marketplace are useful in this regard. From a marketing perspective, tools like SEMRush can help you find who is attempting to compete with you for organic search traffic or paid advertising campaigns.

Indirect competition, on the other hand, can be tough. Indirect competitors include novel uses of seemingly unrelated tools, tiny upstarts with aggressive sales teams, and tools that aren't primarily designed to accomplish what you offer but have bolt-on features that do a reasonably good job of accomplishing the same thing. These competitors can sometimes be hard to find. It's difficult to have a "beginner's mind" and try to envision solutions that don't look or function like yours.

A huge benefit of big data is its ability to surface these indirect competitors. Customers often share emerging brands or novel uses for existing tools on social sites like Reddit. New competition that is small but has momentum can be surfaced on sites like Pitchbook and Twitter. This insight can be difficult or impossible for a human to find, but platforms like Speciate can surface them easily.

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Determining Key Areas of Interest

In Speciate parlance, we call this your "ontology". It's effectively the lenses you seek to apply on a competitive set and the terms within that set. Common ones include:

• Product or service offerings
• Positioning statements or terminology
• Features and benefits
• Associations relative to other terms

One of the benefits of emerging platforms like Speciate is the ability to surface second-order associations, or associations that might not have normally occurred to your team. It's common to start with a first pass ontology, run the analysis, and discover a second layer on top of those associations. It can also often surface completely different ontologies that would not have normally occurred to your team.

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Gathering Data

This is the most obvious benefit of leveraging tools like Speciate AI. Consider the various sources for competitive intelligence today:

Search Engines

Google shows you the results it believes to be most relevant to a phrase. But this is largely a function of the skill of the website owner to optimize their site for keywords, the "domain authority" of the site (how reputable the site is in the eyes of Google), and the number of inbound links coming into that page.

Google has learned to largely de-emphasize social posts, for example. Possibly because they are ephemeral, possibly because any one post doesn't have inbound links going to it (people engage on social primarily through likes, shares, etc. vs. linking to them from their own sites). Google also can't show you content that lives behind a login. So sites like Pitchbook, 10k data, scientific and/or trade journals, and other sources are excluded from results.

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Social Media

Social is a fantastic source, especially for customer sentiment and insights. But it's a veritable firehose of information. Without a tool that can search, analyze and aggregate that data at scale, it can be incredibly difficult to find patterns in social data, forcing analysts to use it for supplemental or anecdotal purposes.

Another consideration is the proliferation of networks. While Twitter, Reddit, Facebook and LinkedIn are often used as sources for data, a frequently neglected aspect of "social" is forum sites – the original "social networks." There are almost always niche-based forum sites for topics of interest. These sources are typically overlooked.

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Review Sites

Review sites are a treasure trove of data, but they can skew results. Most positive reviews are provided with the strong encouragement of the company (sometimes even in exchange for compensation), or by internal members or other interested parties. Negative reviews, typically, are more useful, and can identify areas of weakness. But what can be missing from these analyses are the actual strengths and benefits from objective parties.

Unstructured social sites and bloggers provide much more useful sources of data here. They're less likely to be directly benefiting from the reviews (although bloggers are sometimes sponsored, they typically call this out), and can be a stronger source of objective data.

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Synthesizing Data

Synthesis is historically heavily manual. Because of its time intensive nature, it often is little more than a frequency analysis and cherry picking anecdotal data to support assertions. But using neural networks and machine learning now makes surfacing insights and trends much easier.

Frequency is obviously important. But there's much more you can do with good algorithms and system design:

• You can identify the sentiment of those data points, and do so at scale - critical for looking at social data sources and review sites
• You can do bipartite analysis to look at the data sets across multiple dimensions
• You can do cluster mapping and build network diagrams to find patterns and white space from large data sets

These types of analyses are simply infeasible using traditional competitive intelligence approaches. Now they are trivial.

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Sharing results

Finally, packaging the analyses up for presentation is unnecessarily time consuming, and doesn't provide opportunities for deeper dives or collaboration. But modern platforms like Speciate allow you to dig deeper within data sets using robust visualization and charting libraries, and provide opportunities to collaborate both internally and with experts on Speciate's side.

Competitive intelligence is as important as ever. Companies looking to maximize the insights they gather from these efforts can leverage modern tools like Speciate to streamline their process while surfacing more robust insights.
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Contact us to get a demo and see how Speciate AI can turbocharge your competitive intelligence efforts.