In today’s highly competitive commercial marketplace, competitive intelligence (CI) is key – and not just in marketing and sales. All departments need to integrate this research feature into their remit, including market research, product development, C-suite, sales, marketing, procurement, fulfillment, and more.
The challenge of adopting such a fully vertical and horizontal integrated approach to CI is that each stakeholder group requires a subtly different parcel of information and wants to consume it in a different format, in a manner they find useful. This creates the impression that using competitive intelligence at scale will be onerous and time-consuming.
What Resistance do Companies Face when Incorporating Competitive Intelligence?
It seems like a no-brainer – if you want to beat the competition, you need to source information on what they’re doing. But it’s not just about tracking the competition. Competitive Intelligence is designed to provide you a competitive advantage with a more scientific, analytic, and statistical approach.
But there’s fear of the unknown for companies who have not yet adopted CI. They worry that CI will be expensive, time-consuming, and hard to implement. Yet without it, employees struggle to locate real insights which are actionable. And the truth is – your competitors are already using it, so you’re potentially losing the intelligence arms race before it’s even begun!
Resistance to the competitive intelligence approach isn’t usually due to a lack of available data or technology. There’s an ocean of available data, and a host of software solutions, agencies, and specialists capable of mining it at scale. There are plenty of tried and tested tools within CI too, including:
- AI and machine learning modeling
- Knowledge management
- Predictive analytics
- Natural language generation
- Speech analytics
- Integrations with other systems
Despite this wealth of resources, by putting off implementing CI, some companies are failing to derive actionable insights which can drive highly profitable decisions.
For a competitive intelligence provider, the biggest challenge is to understand the problem their clients are facing, then shape the data they source to render it accessible, understandable, and usable. In a sense, the job of a competitive intelligence professional is not just to gather intelligence and subject it to analysis, but to drive change within organizations. They must create a sea-change in how some companies approach market competition, and at times, it can feel like an uphill struggle.
This is despite CI’s applicability to both established businesses and start-ups. Regarding the latter, Alison Murdock writes in Forbes, “Building a company is like entering into a new, all-consuming relationship. Investors will ask about the market landscape, and competitive intelligence provides valuable information upfront.”
Yet executives are keen. PwC found that 85% of C-suite survey respondents claimed that AI would be a “mainstream technology” in their organizations in 2021. It seems likely that most of the resistance is coming from further down the corporate ladder.
Most competitive intelligence programs that fail, do so because they have not delivered actionable insight or real value to their clients. Let’s look at that a little more closely.
The Ultimate Guide to Integrating AI into Competitive Intelligence Programs
How Competitive Intelligence Can Deliver Value
For competitive intelligence teams to succeed in providing insight-rich data which drives change, six aspects need to be considered. Let’s run through them.
1. Completeness is Vital
A laser-targeted AI should be used to source information, and ensure focus and completeness. Value-generating decisions can’t be based upon incomplete information.
Whereas previously teams would manually source competitive information, the volume of data and the rate of change within various industries make this impossible. Using AI for data gathering allows CI team members to concentrate on data curation and on turning insights into actionable steps instead.
2. Domain-Specificity is a Given
The AI which is used must be domain-specific, rather than roam widely, gathering irrelevant data, much of which must be discarded.
Every company has a unique market sector, product line, brand identity, and business approach – that’s how they stand out within their marketplace. Therefore, a client’s competitive intelligence AI must be trained within a ring-fenced domain which is as specific to their business model as possible. This ensures the data derived has a high relevance and applicability.
3. Data Presented in a Situation Appropriate Manner
Formats for presenting data must be customizable. One size simply does not fit all. To motivate a department to make good use of the information you’ve sourced, it’s vital to present it in a friction-free manner, which means it must be clear, complete, insightful, and actionable.
By removing barriers to its comprehension, you increase the likelihood of your information’s adoption. You improve the chances that the intel will be acted upon. These formats will vary team by team and department by department – battle cards for sales teams, marketing campaign analysis, or patent analysis aimed at your R&D team, for instance.
4. Distribution and Integration are Key
Intel must be distributed and integrated. Competitive Intelligence pros need to bring the data to the relevant parties when and where they need it, rather than expecting users to go hunting for it, or launch it on a platform they’ll never have time to access.
Again, what works will vary from team to team. For some, email alerts will be best, for others, customizable newsletters will be more appropriate. You can integrate data into CRM platforms or release pithy insights as chat topics.
5. One Source of Truth
However, there must be a single locus of information for those who need access at scale. CI teams should adopt a centralized platform to host all intelligence and to ensure completeness so that decisions can be well-evidenced.
This means users won’t have to scour different sites or databases for information – public data, as well as primary and secondary research and syndicated records, are all there in one place.
6. The Human Touch
Curation is vital, given the oceans of data that a good competitive intelligence AI can source. Even when such an AI is laser-focused, not everything it mines will be relevant. This is where human domain experts come in.
Depending on your specific use case, a domain expert will research the best sources of CI and the criteria by which relevance will be assessed. Some competitors are especially difficult to derive insight from, but domain experts know how to wring every drop of value from competitor sites regardless.
A third-party CI expert (i.e., not in-house) will produce a more unbiased approach to win/loss analysis, so it pays to engage a specialist agency from outside your company to perform this role.
There’s also a tendency to leave briefs wide open because executives worry that they’ll miss something and therefore fail to derive the best ROI from their CI operations. This can often leave them with a mountain of irrelevant data to look through. A CI expert can help significantly with better filtering.
Expert data curation will vet incoming data and filter it per use case. They can vary their approach according to each competitor and product line you’re interested in.
Conclusion
If you’ve ticked off all the items in this competitive intelligence checklist, then you stand a good chance of being able to derive actionable insight from your competitor data.
Creating meaningful change requires a thorough and focused approach, using a combination of domain-specific AI and human data curators, something that Evalueserve clients appreciate very well.
