To be honest, data is not directly fueling the retail industry. Analytics is the one who is doing it. However, to get the right analytics, you must first collect data. So, indirectly and technically, data is important because it is the first step in the overall Analytics process.
The adage the customer is God still holds in the retail industry!
And today’s customer is well-informed and has access to all of the details that influence his purchasing decisions. One of the most effective ways to win customers today is to target them with a customized and personalized approach that provides them with exactly what they are looking for, and only data analytics can help in this case.
Analytics enables retailers to transform their data into meaningful insights that they can use to define new go-to-market strategies with a more customer-centric approach.
The following are a few examples of how data and analytics fuel the retail industry:
- Understanding Customers and Their Behaviour:
Today, data not only allows for a better understanding of customer behavior but also aids in adapting to changing buyer behavior. When data is managed properly, it generates a 360-degree customer view, which allows us to identify buyers who are actively involved in purchasing behavior and then place products and services firmly in their line of sight, allowing them to easily progress to the next stage of their buying cycle. Data improves retailers’ understanding of customer behavior and allows them to target them more effectively.
- Demand and Supply Forecasting:
To run a successful retail business, demand forecasting is essential because it provides a possible picture of future demand, allowing you to start planning everything else from production, inventory, and supply avenues to meet the market’s expected needs.
Forecasts are typically made at various levels of granularity – ranging from quarterly to hourly – to support various planning processes, execution strategies, and business decisions. Having said that, no one can deny that more detailed forecasts are always extremely valuable, and this is only possible with Data Analytics implications.
To effectively execute store capacity planning for a retail outlet or do store replenishment, retailers must leverage the demand forecast in all planning-related initiatives to eventually get more sales with better product availability, reduced spoilage (especially in the case of perishable goods) with better stock allocation, increased inventory turnover with less need for safety stock, and the list can go on since there is a lot more discipline that can be brought to the retail operations with having right forecasting of Demand and supply that is powered by data.
- Customer Engagement Evaluation:
Data and analytics assist you in uncovering the true information about your customers, which can then be optimized to provide a better customer experience and can even be easily monetized if used correctly. Today, more than 80% of customers are willing to pay more for a better customer experience. As a result, measuring customer engagement based on the experience you provide is critical.
Metrics such as how much time our customers spend waiting in lines, how much time they spend in stores, their engagement with loyalty programs, the feedback we receive, and the average net promoter scores provide information that can be used to improve our shoppers’ overall engagement. Taking customer experience from Ooh to Aha I’m sure one must have heard the phrase “data is only as powerful as what you do with it” before.
That is, if we use data to its full potential, we will be able to learn from our mistakes and implement changes that will allow us to provide delightful “aha” moments and positive long-term experiences to our customers. Personalization is one way to provide a better experience, which is possible when we capitalize on data about our customers’ preferences.
- Customer Churn and Retention Prediction:
The best way to remain profitable and grow significantly is to take care of our customers and keep them coming back. The first step is to keep track of our customers’ churn and retention. Analytical customer churn models that are data-driven and use behaviors such as customer purchase intervals, upgrades, cancellations, follow-ups, and overall engagement throughout the tenure can help us predict when a customer will stop using our products and services. Using analytical models, we can identify a unique score that is assigned to each customer and will assist us in determining whether they will continue to use our products or not, allowing us to make pivots accordingly.
- Prize Optimization:
Prize optimization is critical in retail because it directly leads to revenue optimization. Retailers use data and analytics to determine how customers react to different prices for their products and services offered through various channels. To create an effective model that shows the impact on sales when product prices are changed, we must use a combination of historical and current pricing, as well as consumer purchasing data. The more relevant the data, the more accurate the model, and the better-equipped retailers will be to determine the best price points for the products.
- Improving and Evaluating the Marketing Mix:
Today, in the digital era, where the majority of shopping is done online, it is critical to re-evaluate the marketing mix and determine whether your brand and its products are a good fit for the futuristic e-commerce world. Fortunately, that can be evaluated and improved today by integrating marketing mix models with analytical models and techniques to provide multichannel impact analysis that can be utilized to drive and assess the success of the applied marketing mix. Such analytics also includes indicators that can be used to improve and transform the old marketing mix into something that is more relevant for success today and in the future.
Conclusion:
To be successful in retail today, we must rely on advanced retail analytics, metrics, and strong KPIs to guide and support critical customer-centric business decisions. And, for doing so, retailers require data-backed processes that can harness the power of retail data in their analytics journey to deliver a good shopping experience to their customers, which can improve their satisfaction, loyalty, repeat purchases, and ultimately makes the customer more engaged and delighted. Not only that, but it also contributes to the overall revenue growth of the company.
