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Since the turn of the century, the age of information has seen masses of data dumped into disconnected silos across many retail organizations. Limited access, capability, focus and free time has meant that until very recently we were only able to skim stones across the surface of this pool of potentially rich insight. However, where once IT systems creaked to the breaking point whenever a non-batch data request was made, the digital transformation of customer data management is now enabling the development of interconnected information ecosystems and finally rich customer insight. With loyalty programs in which customers self-identify, retailers can attribute disparate pieces of data across multiple silos and touchpoints to build a complete picture of their customers – what we call the Golden Profile. With this Profile, retailers create relevant messaging to reach their consumers at key moments in time along their journeys.
Advancements in technology, storage capacity and processing power promise to unlock a future where advanced analytics can drive personal interaction between retailers and their customers, and the challenge now is to collect and action upon the data effectively, efficiently and within budget to ensure that it forms the cornerstone of a profitable retail future
However, a commitment to applied customer insight is a base requirement and not a game-changer in and of itself, and your approach and investment must be considered within a clear strategic framework to avoid it becoming a costly black hole.
At the End of the Digital Rainbow
Retailers must move from a mindset where they feel inundated with data to a mindset where they feel data-rich, which can be derived from a well-articulated data strategy. This approach requires clarity on what you need to know first, agreement on who will have access to this information and then collective application of insights across the business to drive the KPIs relevant to your customer strategy. Soon, it will not be acceptable for any organization not to know its customers intimately; who is important and who is not, who will grow the business and who is transitory.
As the data rush intensifies, so does the need to manage the data appropriately by being open about what information is being captured and how you intend to use it. Permission, protection and trust are key elements in the ongoing collection and use of customer data and the value exchange principle will be essential here if we are not to kill the golden goose through inappropriate or unethical usage. We should all retain sight of the fact that ultimately customer data always belongs to the consumer and its use can only continue with the trust of the customers themselves.
Advanced Analytics Unlocks the Door to Riches
Predictive analytics sit behind much disruptor behavior and - as we have stressed - traditional retailers must compete by harnessing advanced analytics to explore what lies behind the behaviors of customers.
Continuously repeating the same “rear-view mirror” analytics can lead to a retailer becoming more effective and efficient in those specific customer areas, but to break the mold, retailers should trial metrics that can shape future customer experiences.
To provoke thinking in this area, Aimia uses a Share of Wallet analysis to uncover where else your shoppers are spending their money and why you are missing out on this spend, even amongst your top customers. Knowing more about the “dark side” of your customer’s spending delivers understanding and change, and is a breakthrough challenge and future opportunity.
The key for every retailer is to unearth a series of actionable insights that allow them to build a sustainable competitive advantage. By using AI and Machine Learning, advanced analytics will pick up behavior differentials that can go beyond projections to predictions. This allows customer demand generation strategies to be constantly refined as you execute based on learning and predicting what customers will do next.
Predictive metrics can also be used to streamline operational issues through real-time and transparent demand planning. For example, sales and traffic analyses that help to optimise availability and to build efficient last mile solutions.
Segmentation Drives Demand Strategies
As customers increasingly expect retailers to act on the information collected about them, retailers are responding by designing personalised customer engagements which strike a balance between empowering the customer and guiding them through their journey. Gen Z coming of age will transition shopping to the next level of digital personalization.
Achieving the goal of N=1 individual level segmentation is important for marketing, communication and engagement policies. However, in the commercial buying and merchandizing arena this level of granularity is too low, and it is more useful to group customers together into behavioral segments driven by their basket level purchasing behavior.
The Aimia Advanced Segmentation (AAS) tool enables this to be achieved quickly and through a transparent and retailer directed process. The segments that are developed immediately become the focus of management information dashboards and the terminology is adopted across the retailer, enabling aligned thinking and target management.
AAS allows dynamic segmentation which plots the life journey of customers through the segments as their needs change. This fluidity is a strength as machine learning techniques can help to spot when clone customers might be most likely to require different interactions. Segment level and individual level strategies can also focus on lapsed behavior management to prevent churn.
Bringing Your Data to Life Requires the Human Touch
It has been said that customer understanding is the new currency across retail, and it is obvious that the more we know about our customers the more appropriately we can meet their needs, and hopefully retain their business. However, once a customer data platform is installed, the data will just sit there until you can recruit the right team to orchestrate the use of advanced techniques to unravel the individual data strands of each customer interaction and interpret the findings.
Despite a huge growth in the number of advanced educational establishments offering courses in data science (or similar) there is a real-time shortage of personnel to operate in this area. This bottleneck will lead to all but the most progressive retailers being unable to exploit the possibilities held in their customer data depositories as they will be competing for these new miners with most other industries. Worryingly, this could lead to an over-reliance on automated systems which, without clear human direction, may end up going into analytical cul-de-sacs.
Forward-looking retailers are already putting in place graduate academies and centers of excellence to hothouse the necessary skills within their own establishments. This will attract new talent and offer the opportunity to retrain current team members and ensure they are future focused. Get the full Building a Customer-First Retail Future whitepaper now, or email [email protected] for more information.