10 common misconceptions about customer intelligence


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A few months ago, I listened to an interview from the founder of a new customer intelligence platform. This start-up is addressing the field of customer intelligence from the angle of automating buyer personas. Cintell’s aim is to provide a customer intelligence cloud-based solution that helps businesses create, manage and share data-rich SmartPersonas.

There are other customer intelligence solutions been provided by companies such as IBM and Clarabridge. Nonetheless, this field is still considered nascent in methodology and automation.

Marketforce added that this business area is still new and has an emerging approach for generating customer insight. They further enthused that customer intelligence enables companies to have a sole view of their customer-related information.

In the midst of the emerging platforms striving to bring clarity and confidence to this interesting approach, I believe there are a couple of misconceptions that needs to be looked at.

10 common misconceptions on customer intelligence

1) Customer intelligence is all about big data: While big data is an integral part of a host of business processes, certain individuals tend to view customer intelligence from the lens of big data. Shashi Uphadhyay of Lattice engines posited that customer intelligence focuses on the past why big data looks more at the future. In addition, I do believe big data is an integral element of customer intelligence but does not completely equate to CI.

2) Customer intelligence can be completely automated: We are currently seeing the automation of customer intelligence. The sentimental analysis is an important subset of customer intelligence and a research carried out on Starbucks by Matt Rhodes revealed a neutral sentiment score of about 80% from respondents. This inspired him to tag sentimental analysis as a complex beast. Some proponents within the field believe that automated and manual research should be combined to achieve a more rounded sentimental intelligence due to the complex nature of humans.

3) Customer intelligence is synonymous to business intelligence: Shashi from Lattice engines drew a similarity between CI and BI. He argued that they both focus on the past. Regardless, customer intelligence is not synonymous to business intelligence but could be considered an element of business intelligence. The main different between BI and CI could be viewed from the source of data. While BI could gather data from sources such as suppliers, customers, employees, competition and a host of other bodies. Customer intelligence gathers customer data mostly from customers via mediums like social media, VoC and market research. BI is a lot broader than CI.

4) The bigger the data the deeper the intelligence: Big data is the reoccurring buzzword that seems to have a deafening impact on the business world. Some people believe the bigger the data the richer the insight. Whilst this could be true but it is quite obvious that too much data could be a little overwhelming. I would say the well-defined the business goal the deeper the insight.

5) CI is more about acquiring new customers: Research carried out by leading market research firm Ipsos, indicated that it cost five times more to acquire a new customer than retain an old one. Customer intelligence will not only be useful in helping businesses acquire new customers but also retaining existing ones.

6) CI is all about customer lifetime value: Customer lifetime value is very important to most businesses. This has inspired these companies to adopt CI platforms and methodologies in predicting the lifetime value of customers and also churn rate. In this modern era, CI will not only help in predicting customer lifetime value but would also look at customer referral value. This is the age of customer advocacy and CI will help in predicting customers that are more likely to be brand advocates.

7) CI can be achieved with a single application: Companies try to cut down on cost and end up seeking one solution that meets all her needs. This proves to be problematic as Tom Davenport attests to this by stating that CI approaches are tailored along two dimensions. Some platforms are technologically aggressive with competencies in online sentimental analysis, mobile or location-based offers, video analytics, uplift modelling and a host of others. On the other hand, he added that there are business-aggressive applications that are characterised with predictive modelling of customer service episodes, A/B randomized testing, attrition modelling and a couple of other elements. The CI applications at present are either stronger on the business-aggressive angle or the technological-aggressive dimension.

8) CI is more correlated with social intelligence: Social intelligence is conceived as the capacity to negotiate complex social relationships. Customer intelligence is geared towards helping companies understand social wirings of customers to better foster a stronger business-customer relationship. Whilst social intelligence has a great relationship with CI, the bane of the correlation is emotional intelligence. Individuals that work for these companies need to be aware of their emotions before understanding, interpreting and predicting that of customers.

9) CI is all about understanding customer drivers: While CI seeks to understand the drivers behind customer behaviour; this does not seem to end at this stage. Businesses seek to understand what motivates customers to act the way they do. Understanding the drivers is important but ascertaining where these customers are driving to is more important. Are they driving towards churn or brand advocacy? These needs to be clearly defined.

10) CI success depends on the analytics team and senior management: While the analytics or data mining team, analyse the data to find patterns to help C-suite executives make informed decisions, those at the frontline also have a part to play. Within a retail sector, the frontline staffs have a great role in understanding and interpreting customer behaviour. These staff members have to be equipped.

It is widely believed that the aim of CI is to understand the motivations or emotional triggers of customers. An adequate understanding of these drivers enables businesses to make strategic adjustments that facilitate growth. However, this is an emerging field with complex approaches and dividing interpretations. Do you think there are other misconceptions of CI?


  1. As an addendum to #6, most companies think that they can manage LTV and customer life cycle with fairly simple insights. It takes understanding the emotions that drive memory and customer decision-making, especially when that set of experience perceptions is negative and leads to churn: http://customerthink.com/optimizing-lifetime-customer-value-or-dont-worry-use-churn-models/

    Also, as noted in your post, LTV optimization takes identifying, at a very granular level, what shapes and maintains customer advocacy behavior: http://www.qualitydigest.com/inside/quality-insider-article/customer-advocacy-behavior-measurement-part-1.html

  2. Hi Michael,

    Thanks for your comment. I share your sentiment on the fact that LTV seems to be oversimplified. We are in an age of referral or advocacy marketing. Companies need to have a broader picture when weighing the overall value of a customer.



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