Predictive Analytics, As Your Customers Already Live in Future

0
573 views

Share on LinkedIn

Retail Analytics

Increasing number of channels and multiple devices are decreasing the opportunities for retailers to build a long-lasting & significant relationship with consumers. Companies struggling to make two ends meet, continue taking decisions by looking back at what went wrong. Instead, leading organizations are using predictive analytics to predict the future—and get it right. It helps them to anticipate customer behavior with high probability and improve the only relationship that really matters; the one they have with their consumers.

Technology infrastructures such as CRM software, customer database, an eCommerce platform, or the ability to mine data from social media; are the capabilities that retailers have had for decades. But back then predictive analytics was used to manage inventories and optimize supply chain and distribution networks and not for understanding customer behavior. It might be the reason that most of them today struggle to establish a relationship with consumers. In spite of brands spending millions of dollars, fickle consumers with just one experience that’s not to their liking, turn away to never come back – ultimately wasting all the investment made to build an enduring relationship.

Predictive data analytics uses current and past data to enable you to make predictions about the future and several unknowns. One can see the likelihood of a coming event or a specific situation, given the data is analyzed. Once a company incorporates retail analytics into predictive models to make decisions, it can change the way it operates.

Mark & Spencer, used predictive analytics reduced expenses on inventory and also ensured that the stock they are buying converts into sales, and not a dead investment. Retailers with analytics on their side can focus on areas of high demand, quickly pick up on emerging sales trends and optimize delivery to ensure the right inventory goes to the right store. E-commerce players can stay ahead of customer preferences, streamline supply chain management and reduce inventory expenditure while expanding the margins.

However, to be successful in the long term, predictive analytics should be an ongoing process – as both internal and external factors are constantly changing. Data models are required to be reviewed and revised to accommodate concurrent changes. Retail companies are required to persistently look at what is occurring in the marketplace and what new information is available, as access to new data sources, means new outcomes. Companies cannot build a predictive model once and say “we are done; because what worked just last year may not work today.

Applying predictive analytics at operational levels

Retailers have been apt at using data to make predictions, like using sales from previous years to estimate next year’s inventory, forecast sales – average cart size and items most often purchased together etc. But today predictive analytics means much more than that. Etailers look at each individual and evaluate their purchases in real time to accurately predict what they would buy based on their specific buying habits & purchasing behavior. Getting predictive with help of data and analytics helps them to present every customer with a set of offers/products at certain prices, with more probability of it getting converted to sales; automatically and at scale.

Ecommerce companies are investing increasingly in consumer digital and marketing technology. Hiring outsourced data analytics capabilities or seeking assistance from data analytics companies, is been looked upon as a step to profitably reach, grow, and retain profitable customer base. It’s interesting to know how it can help retailers to develop a bond with their customers.

Predictive analytics to bring and keep profitable customers onboard

• Access to customer information

Technology has empowered consumer goods companies to build and maintain stronger bonds with customers. Right from everyday apparels to luxury brands, home appliances to sporting goods, and personal care to toys; companies selling goods with help of retailers now have direct access to consumers of their products, and a massive amount of valuable data about customer preferences and the entire buying process as well. Consumers make transactions through mobile devices, social networks, leaving behind their digital footprints making their preferences known – only if retailers know how to interpret those signals.

However, possessing all the huge data does not suffice. It does not mean that the predictions made about behavior are precisely correct. It is the predictive analytics that utilizes the new data to know the customers better & design experiences to delight them across multiple contexts, channels, platforms and of course devices.

• Design relevant products & services

Customer choices are not limited to the products/services on a shelf at a local store. Consumers can access all brands and all products anytime they want, and that too with near perfect information about the product, with help of reviews, ratings, product comparison sites and online shopping. More product information means more choices for consumers, but more complexity for brands striving to deliver great customer experience.

Gaining knowledge and understanding about customer preferences helps companies to design more relevant products and services, and repeat the experience that made them stay with the company. Predicting what a customer will do in a given interaction with a high likelihood of probability is important. Without predictive analytics, there is no guarantee that the company will even get an opportunity to interact with the customer before they make a decision.

• Anticipating customer desire

Predictive analytics is all about intelligence and insights about consumers past behavior, and their expectations & desires. Data available, inside and outside the brand’s environment, can be used to derive historical patterns and predict the chances of particular outcomes in form of probability scores – calculated & assigned to individual consumers or segment of customers. It helps in expressing the chances of a consumer taking specific actions such as buying a certain brand of products or even choosing one offer as against another.

Experienced predictive analytic consulting firms have enabled companies to deliver content (read offers) specifically developed for a particular segment of consumers. From among a heap of different products to choose from, predictive analytics narrows down options to a critical few, which a particular consumer will respond to, interact with, buy and promote within the social network followers.

Enterprises capable of stepping back to think and plan to leverage data and predictive analytics to transform the way they run their business, make it run faster – leaner and smarter while improving the profit margins; are the ones to benefit the most in the dynamic market. With such insights retailers and etailers can predict with extreme precision almost exactly what every customer wants, and deliver a personalized experience for each one of them. But they must ask the question: should they?

Predictive analytics is not a function

And because it is not a function, a mere insertion of it in your existing business processes is just not possible. Instead, forming a strategy and seeking outside help around the collection, governance, and analysis of information is required. This would enable a seamless flow of predictive analytics services and benefits to various functions and groups across the organization.

LEAVE A REPLY

Please enter your comment!
Please enter your name here