‘Data is the new oil’ – an industry cliché that everyone quoted at every remotely relevant discussion. One that you’re perhaps tired of hearing. But is it really the new oil? It seems fine at a level where it acts as the key economic driver but nothing beyond that really. And why am I saying that? Read on to find out.
To turn oil into money, one needs to drill, extract, refine, and then sell it. However, with data, though we have a ton of it, we either don’t know or we’re not able to extract the value from all that data to help generate revenue. For example, with this mass exodus to digital, grocers who have an online presence were able to capture a lot of customer data. However, they are unable to leverage that data to enhance customer experience which would result in improved revenue.
The reality is that over 85% of retailer decisions are gut-based, as only 43% of the data is deemed actionable. So, data is worthy only when it can be leveraged to make appropriate decisions in real time. And that’s where the need to move from data-driven to decision-led arises. And algorithms make this possible.
The constantly evolving business environment, customer needs and preferences, competitive landscape, and many such factors add to the complexity. There is a plethora of opportunities in this dynamic marketplace, but retailers need a tool that helps them convert data into actionable insights in real-time to make those contextually relevant decisions. They need AI to transform data into money.
Inaccurate and Delayed Decisions Are Costly
Customers today expect individualized experiences. With little product differentiation, the shopping experience is increasingly becoming the key differentiator for retailers to win the long-term loyalty of customers. In order to cater to customers with the right content, at the right time and on the right channel, retailers need actionable insights that are contextually relevant and up to the moment accurate.
Relying on dated approaches of making decisions based on instincts or dated reports and basic aggregated data is no good. This has cost retailers dearly – they’ve quickly lost customers to more-savvy competitors, resulting in revenue loss and ultimately the shutters going down on the business.
Data is a Fundamental Challenge
While retailers have data coming in from various sources, the complexity is high. Data is stored in siloed systems that don’t talk to each other. Retailers, therefore, lack a single source of truth. They have CRM, Data Warehouse, and ERP systems, but these systems don’t provide actionable intelligence. They require manual intervention and are too complex to manage as well.
Here’s where a Customer Data Platform (CDP) brings about a change. The platform unifies data from across sources to provide a single view of the customer that is complete and current. CDPs create granular segments and analyze every customer transaction, behavior, and preference that retailers can leverage to make decisions for personalized engagement.
Decisioning Made Possible by Algorithms
In a day and age where customers expect contextually relevant experiences in the moment, retailers must equip themselves with tools and technologies that arm them with what is needed to meet customer expectations.
Customers today expect to be served as individuals whether it is while shopping online or in a store. They expect relevant product recommendations based on their taste, offers that align with their need, and communication in their preferred channel, at the right time. To cater to this, retailers need to make smart, intelligence-infused decisions while the customer is in their journey.
It is common for retailers to have separate systems in place for point-of-sale, eCommerce, and loyalty – all containing important insights on shopper engagement. How do we stitch all this data and leverage it to unearth insights for competitive differentiation? Even the largest retailers that have more resources and tools usually have too few data scientists and analysts working with overly complicated tools to support their decision-making.
Algorithms are your answer. AI has the power to bring all the data together and analyze it to cull out deep insights at an individual level, at scale.
Algorithms with real-time decisioning capabilities support continuous testing, ensuring that the right decisions are being made automatically. They continually test and evaluate strategies to determine the winner for each user interaction and business KPI. The models adjust for subtle changes in behavior, inventory, pricing, etc., and provide complete transparency into why a decision was made.
Retailers could expand their customer base by attracting a similar kind of audience as their loyal customers and reverse churn behavior by sending the right offer on the customers’ most preferred brand and product. They could delight existing customers and increase basket value by recommending the right handbag that would go with the dress the customer just bought and push the perfect burger combo offer to the customer at lunchtime based on their location.
How a Large American Grocer Enjoyed a 3% Increase in Revenue with Algorithmic Decisioning
The grocery chain struggled with how to implement and sustain a data-driven, targeted marketing strategy. In their earlier state, they at best sent weekly email flyers to their customers on all the offers without any customization based on past purchases and preferences.
Multiple channels were deployed, however, there was no integration among digital systems leading to a lack of a unified customer experience across channels. They were unable to run multi-channel campaigns, and email campaigns were run manually with no holistic understanding of performance. There was no personalization and targeting capabilities on eCommerce and the mobile app, leading to low (~10%) digital penetration and engagement.
To start with, Algonomy’s CDP provided unified customer profiles. Its intelligence layer, supported by machine learning algorithms, helped create granular customer segments by applying RFM modeling. The CDP helped understand customer journeys, identify products of interest, and utilize propensity models to gauge the likelihood to respond, buy, and churn.
Armed with deep customer insights, the grocery chain adopted a personalized marketing approach that was curated to each customer’s preferences, transactional behavior, lifecycle stage, and promotional activity. They were able to achieve a 4X increase in mobile app usage and a 3% increase in revenue through an Algorithmic Decisioning approach to personalize customer engagement.
From Data-driven to Decision-led
It’s time for retailers to shun outdated BI tools and espouse AI to make critical decisions. Retailers are time-starved, and an overload of backward-looking reports is of no help to the decision-makers. Contradictory findings from disparate solutions that are poorly integrated need to be a thing of the past.
Instead, retail decision-makers need intelligent, actionable insights delivered in an easy-to-consume fashion. They need comprehensive, predictive (forward-looking) insights with prescriptive recommendations. In summary, retailers must move from being data-driven to being decision-led with AI at the center of it.
Algonomy is coming to NRF 2022 – Retail’s Big Show to unveil the industry’s ONLY Algorithmic Decisioning Platform.
Meet our experts at Booth #6403 to dive deeper into Algorithmic Decisioning and its application in your business context. Book a meeting here.
