How CPG brands can retain consumer loyalty through data analytics
An interesting thing happened to consumers during the COVID-19-induced lockdown. Their favourite 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 personalised 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 rifer than ever before.
This trend will only get sharper with more and more customers renouncing the commonplace CPG labels in favour 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 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 localised 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.
Incubate new data analytics use cases
Thinking beyond business-as-usual!
Cx Os should determine the business goals for their CPG brands before embarking on their analytical journey. Once the business goals are set, analytical engines can be designed, developed, and integrated onto the enterprise-wide data analytics platform.
To get to the next level, CPG brands must incubate new use cases to capitalise on their data analytics investments, some of which include:
1. Hyper-personalising customer touchpoints, from marketing to POS
With holistic data analytics frameworks, brands have a fresh opportunity to anonymized master profiles for each of their customers while decoding their buying journey. And with this, targeted messaging gets easier across the touchpoints, right up to omnichannel points of sale.
2. Encouraging new product offerings
With strong data engineering comes a deeper understanding of consumers’ triggers and barriers. CPG brands have a fresh opportunity to incubate the use of deep data analytics and data engineering in new product design. When new products are designed on the back of these data engineering concepts and specific customer needs, they would naturally fly off the shelves faster. A win-win proposition, if there ever was one.
3. Adopting advanced supply chain practices
There is no denying that DTC brands have delivered unprecedented convenience to consumers. Now that consumers are used to this convenience, relevant CPG brands can compete only by strategically executing their own DTC programs.
Removing the middleman from CPG transactions by using an enterprise-wide data analytics platform would ensure that consumer preferences are effectively captured informing Brands to update their strategies and mode of operation to accommodate the demand.
Rethink and embrace new data-driven strategies
Competition is heating up. Brands that are founts of intelligent strategy, customer convenience, personalis ed consumer experience, operational nimbleness, and speedy execution are the only ones that will survive in the long run. While the DTC category was quick to redefine itself during the lockdown and gain on all these fronts, most CPG brands still lag in one forte or the other.
Today, investing in data science, building data engineering roadmaps, and implementing analytical engineering for every single business function — from product development and marketing to supply chain and retail promotions is not only critical to the lifecycle of a business but also a sure shot way to ensuring the highest RoI.
It is a new world for CPG brands and their customers. And the ability to choose the right data and analytics engine will set the winners apart. Are you ready?
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)