I am often asked, what is that role / skill / technology that is pivotal to creating an impactful Customer Insights capability?

As Head of the Customer Insights Solutions team at Acrotrend, I am regularly involved in hiring consultants who can provide the best-fit solutions to our clients. As a customer analytics consultant myself, part of my role is to equip our clients with the knowledge to support and grow their data analysis initiative, helping them build the capability within the organisation. Data for customer analysis is everywhere, in multiple sources, in many shapes and forms. However, to drive impactful business action by generating key customer insights from these data sources is not easy!

Investing in technology and customer analytics projects is only a small part of the puzzle, as even the best of technologies will not give you the returns if you do not have the right people in the team – the subject matter experts. In this case, there are multiple titles floating around these days such as Data Scientist, Consumer Analytics Thought Leader… I could go on.

I see the role as multi-disciplinary, a rare combination of IT, business and soft skills and depending on what the organisation has set out to achieve and where you are starting, Customer Analytics experts need these skills in varying degrees of proficiency. Below are the top disciplines I look for in a Customer Analytics expert:

Business Analysis

Consumer insights from business analysisI strongly believe that we are all analysts in our own ways. From programmers to testers, leaders to managers understanding the business industry that we are in, the business strategy and vision of our organisations and the commercials of that specific business is of utmost importance.

To understand traditional and newer ways of customer process marketing, sales and service are integral so that we are able to appreciate the impact that customer insights and analytics can have on an organisation. This is almost a pre-requisite for any Customer Analytics expert but also found to be lacking in most cases as well. Added advantages are decent levels of industry and sector knowledge and experience of how other organisations in similar situations have used analytics to their advantage.

Analytics Techniques and Application

customer analytics techniques

Depending on the industry domain, data types, expected accuracy of analytic results etc, high-level or more involved analytical techniques and models might be required. Thus, knowledge of a variety of techniques and more importantly the ability to recognise and recommend applicable techniques/models to generate the required insight is a must.

Eighty percent of analytics problems can be solved by simple techniques applied rightly, or an ensemble of a few techniques to achieve the expected outcomes with desired precision and accuracy. Ability to blend the supervised and unsupervised techniques to get into the AI level outcomes could be a differentiator. At the same time, expertise and proficiency in the tools to develop, test and production-ise analytics on large scales are a given.

Working with Data

Anyone working in Customer Analytics needs to have worked a lot with data. That data could be of different types, like structured, unstructured and semi-structured, static or in continuous streams. It could be in your systems and network or outside on public forums, social networks etc.  

Customer Analytics experts must know how to navigate through the data ocean, understand which data will be useful and how the data needs to be prepared for further use. This also means that decent skills on querying the data from their databases/sources – like SQL, NoSQL skills are required.


Customer Analytics professionals will need to work with multiple functions across the hierarchy. They will need to work with system owners and IT departments to obtain the data for analytics, with customer-facing functions like sales, marketing and services to understand the problems and challenges and desired insights.

They will need to be part of the business case preparations, manage expectations with sponsors and communication of insights to business users and heads of departments. It is, therefore, crucial to have commercial sense, big picture views, story-telling and visualisation skills using the data and the insights.


Successful Customer Analytics programmes follow an interactive and incremental approach. To be able to work in the field, professionals need to be able to carry out multiple experiments to validate business assumptions, have gut-feelings and be able to offer hypotheses.

Not all experiments will give the expected outcomes and Customer Analytics experts need to know the value of failed experiments and should be prepared to learn from them and start afresh with that experience to back the next experiment. Patience, passion, creativity and readiness to face failures as a team will define the success of this role in the long-term.

It is rare to find all the customer analysis skills above in one individual. If you are lucky to find that one person, you’ll need to be extra lucky to have that worker sustained in your organisation, culture and environment.