How CPG brands can retain consumer loyalty through data analytics
As DTC brands gain more popularity due to better convenience and a personalized experience, CPG brands are struggling to retain their customers. Here is how CPG companies can kickstart their transformation journey to compete.
An interesting thing happened to consumers during the COVID-19-induced lockdown. Their favorite CPG brands disappeared from their pantry shelves. Reliant, now more than ever, on new channels instead of brick-and-mortar stores, the pantry owner discovered DTC (Direct-To-Consumer) brands.
With sheer convenience and personalized experience, DTC brands made their way to household pantries across the world.
Although DTC brands have been challenging traditional CPG models for some years now, the lockdowns were a defining moment in this disruption. Shifting consumer loyalties dealt a severe blow to traditional CPG brands, and the battle of the shelf turned more fierce than ever.
This trend will only get stronger with more and more customers renouncing the commonplace CPG labels in favor of their new, lesser-known DTC variants unless the CPG companies kickstart their journey of transformation right away. But, what does this transformation really entail?
The question most CPG brands are asking themselves is how they can reverse this change and go back to enjoying the same customer loyalties they have experienced for decades now. The answer can be gleaned using data analytics. The new-age consumer is researching, inquiring, shopping, and otherwise engaging with CPG brands online, producing brand new data sets every minute.
Brands can tap into this rich, massive trove of information to precisely decipher the consumer journey, and design and deliver distinctive experiences that appeal to the unique demands of the customers.
Today, devising an analytical strategy from CPG data is the next big challenge.
Data engineering best practices to retain consumer loyalty
While most CPG companies have built analytical engines for business decision-making, different functions still work in silos, limiting visibility to drive concerted business goals. Data structures are distributed and create hurdles in delivering benefits. Buying or building localized analytics solutions drain the company of its resources without delivering the expected RoI.
Delivering a business goal using an analytics-driven strategy requires a new mindset for decision-making — the move from analytics as a dashboard view to full-fledged, enterprise-wide data analytics platforms. What this means is finding the ability — the platforms and solutions — to unify the data silos into an enterprise-wide data foundation.
Competence in data engineering best practices and a far-sighted data engineering roadmap are critical in understanding the analytical requirements of the companies and architecting and creating a unified data foundation to facilitate the enterprise-wide analytics platform, without disrupting the existing workflow.
The unified data should comprise the omnichannel consumer interaction, the supply chain, marketing, and secondary research data to give brands an all-encompassing view of the new pantry-shelf battle that is playing out on consumers’ devices.
Authored by Manas Agrawal, CEO and Co-founder, Affine
