The Importance of Analytics in Consumer Goods Companies

The combination of big data and advanced analytics offers consumer goods companies countless opportunities across the value chain. It’s important not to view analytics within a vacuum, it should almost be viewed as a philosophy or mantra that runs through the organisation. Once integrated it affects every part of the business allowing senior stakeholders to make data-driven decisions instead of instinct based decisions which have a higher propensity for error.

Big data has the ability to influence a business from HR to logistics. For example, data-driven hiring can allow companies to hire the best candidate for the role whilst focusing on diversity. Diversity of thought and lean hiring will lead to a higher retention rate, lower costs and better future leaders.

Challenges companies face in implementing analytics
McKinsey’s Director, Tim McGuire, defined three big challenges companies face when they try to implement analytics. The first one is figuring out what data they actually want to use. There is a tremendous amount of data that is generated internally. Just handling that alone is a big challenge for most companies.

The second challenge is getting the right skills and capabilities, getting people who really know how to use the latest techniques and the latest statistical methodology to get inside that data and find the real nuggets of gold.

The third one and probably the hardest of all is to take those insights and use them to transform the way the business operates. It does no good whatsoever to actually draw insights out of data if you are not going to change the business decisions you make.

Best practice in implementing analytics
You certainly need to start with a synthesis of the problem you’re trying to solve and then find the data that will help you get there. You need to pull insights out of data, otherwise, you can go on a mindless exploration of a big mountain of data and hope that you’ll eventually find something.

When developing a repertoire of analytical applications for your business, take a consultative IT approach with each department and line of business. Meet with managers to understand their unique needs and goals when it comes to analytics. Build on successes and learn from failures so that the structures and processes you use for developing analytical models evolve and deliver more value over time. At the same time, work to create a ‘culture of analytics’, that encourages the discipline needed to use analytical tools well.

Screening for candidates that can help companies implement analytics
Most of the roles we work on at Loftus Bradford require strong analytical acumen. When screening for candidates for analytics positions, we look for these skills:

1. Experience within a “data-centric” organisation: the likes of Amazon, Bol.com and Zalando who are challenging the more traditional retailers such as Aldi or Ahold. These organisations are aligned from top to bottom on the importance of data across the organisation.

2. Cross business strategic thinking: Data for data sake doesn’t create results. To keep the analysis focused, to validate, sort, relate, evaluate the data, the most critical skill is to have a good knowledge of the domain one is working on.

3. Programming: While traditional data analyst might be able to get away without being a full-fledged programmer, a big data analyst needs to be very comfortable with coding. One of the main reasons for this requirement is that big data is still in an evolution phase.

4. Quantitative aptitude and statistics: While the processing of data requires great use of technology, fundamental to any analysis of data is good knowledge of statistics.

5. Interpretation of data: This requires the precision and sterility of hard science and mathematics but also call for creativity, ingenuity, and curiosity.

Examples of successful analytics implementation
Consumer experience has become an increasingly important part of the consumer goods industry, and many companies are tapping into analytics to figure out their consumers’ likes, dislikes, wants and needs. L’Oréal Paris has been measuring and evaluating its consumers’ experiences when using the brand website. Data analytics revealed that the site wasn’t connecting the company with the consumers on a deeper level nor driving engagement.

Based on this insight, L’Oréal Paris changed site navigation, product browsing and search functionality. A 20% jump in both consumer satisfaction and time spent on site, as well a 75% surge in opt-in registration was the result.

Done right, data analytics initiatives can yield big rewards for consumer goods companies. Gathering the right data and developing transparent models, however, won’t create impact unless companies can also turn data-driven insights into effective action on the front line. Companies must define new processes, which managers can easily understand.