Predictive data analytics is the in-thing in retail marketing and therefore, you need to reap the maximum benefits out of it. The retail sector, just as any other industry, has loads of data that you can use to your advantage. Then, managing chunks of data is a big challenge to retail businesses, because such information isn’t converted into useful, actionable insights. That is why you need to leverage retail data analytics in real-time to make informed and strategic business decisions.
Predictive analytics is an essential part of retail and e-commerce businesses. It is like a magic word that can tell you what your customers prefer next when it comes to product choice. Then, where can retail stores use predictive analytics to ensure maximum outcome and value? Here are the best predictive analytics uses cases in the retail sector:
Customized offers for consumers
Learning about consumer behavior and merging the same with buyer demography is the initial step in using predictive analytics. Retail business leaders can use analytics to provide targeted and extremely tailored offers for particular customers.
With online shopping and the use of data analytics, you can monitor your customer behavior across various channels and keep track of a shopper researching in the online store and then decides to buy your products from your brick-and-mortar store.
The retailers use predictive analytics to come up with lucrative, customized offers and deals to their customers. For instance, you can customize some in-store customer experience and incentivize customers buying frequently from your store. It helps in driving more sales across the majority of the channels.
Monitoring Consumer journey
A consumer’s journey is just like a map that monitors a customer’s experience. It begins when a buyer initially gets in touch with the brand and the process ends with a successful purchase. The entire customer journey maps out the engagement process.
Customer journey is not just about the purchase of a product from your store; it also means a mutually beneficial long-term business relationship. Therefore, you need to track the consumer’s behavior even after he or she has purchased your product. The customer journey map gives managers a fair idea of how leads or consumers traveled across the sales funnel.
Data-oriented insights will help you figure out every consumer’s profile as well as history across different channels. You may keep an eye on a customer’s behavior to understand who your best buyers are, what products they like, how they behave and respond to your retail marketing.
When it comes to predictive analytics, it not only means tracking and targeting your consumers but also customer segmentation. You can make the best use of affinity analysis to group the consumer base depending on common features. Retail merchants can leverage response modeling to test previous marketing spur or marketing as well as buyer response to foresee whether using a specific approach will work in the days to come. When it comes to churn analysis, it informs you the percentage of consumers you did lose with time and the possible earnings lost due to the same reason.
Predictive analytics lets retailers like you forecast a customer’s lifetime cycle value or CLV. These days, retail entrepreneurs like to understand how to foresee customer value in due course of time depending on the customer’s interactions with their brand in the days to come. When it comes to CLV, it predicts a discounted value of a consumer with time and CLV entails understanding previous customer behavior to figure out the most profitable consumers with time.
Pricing is the most essential area of predictive analytics in retail. You need to make the best use of data science and machine learning in real-time to understand the effect of pricing on customers.
The benefits of predictive analytics help retailers to get answers to several questions. These include:
Which is the precise price point to boost retail sales?
What is a consumer’s best achievable price?
How frequently do I run price-focused campaigns or promotions?
What is the probable effect of competitive pricing on retail sales?
Apart from the aforementioned questions, predictive analytics also takes into consideration things like weather forecast as well as sales data in real-time to change and encourage the best product pricing.
Consumer-monitoring technology has helped to use the best retail strategies used by grocery stores to predict consumer behavior. It has also helped them find ways to assess online or in-store customer behavior and analyze the effect of retail marketing efforts.
Did you know that several customer points of interaction could provide you with useful data? These are online stores, social media platforms, credit or debit card swipes, and more. Modern retailers can now access complex and varied data or information related to their customers.
The use of such information, as well as data points, gathered from previous advertising and marketing campaigns would help you develop predictive models to link previous consumer behavior as well as demographics. The goal of such models is to score each consumer based on the possibility of them purchasing specific products.
The complete data process will also give you valuable and useful insights to identify high-value shoppers, creating a CLV, a consumer’s intent behind a particular purchase, the preferred channels, the purchasing patterns, and things like that.
When you have such information or knowledge, it will help you offer customized deals or offers as well as retain new clients or customers. You can strengthen customer retention with loyalty programs that motivate buyers to buy your products and not your competitors.
You can use predictive analytics to learn which of your customers are shifting, even those, who can turn into long-term customers for your business.
No matter whether you have a small retail shop or a sprawling departmental store, or an online store, you need to make the best use of predictive analytics. That is because data analytics is not the only purview of large retail brands such as Amazon. Fortunately, you have advanced technology, which is extremely mainstream as well as affordable to access. That is why even small and medium retailers are using predictive analytics to their benefit.