The retail industry has been going through an immense change ever since e-commerce took over by outpacing conventional retail sales growth. With the explosion of online retail, customers are happier to shop via websites than in-store, getting faster service, personalized experiences, and greater choice. The impact of personalization in the retail industry is so pronounced that it has become an element of customer experience (CX) that is progressively becoming a competitive differentiator in omnichannel retailing. To keep up, retailers need to encompass personalization across all stages of the customer experience lifecycle. Fortunately, exponential technologies like AI and big data are now altering the retail landscape and expanding the toolset available for retailers to respond effectively.
Advanced Retail Analytics – Holy Grail of Personalization Ensuring customer preferences and behaviors to be ruminated is vital for retailers in order to provide data-driven and guided personalized shopping experiences. Thanks to retail customer analytics, businesses are now able to create new and efficient ways of gathering information about their customers by collecting data and analyzing it concerning the customers’ interests, motivations, and actions and then using that information to make recommendations or predictions about why people buy what they do and adjust their strategies.
This drives personalization at the individual customer level. Retail analytics solutions enable them to create integrated and responsive ecosystems – including new partnerships and mergers – and evaluate their strategies in the rapidly changing global market, which can help position them for success. Benefits of Personalization Using Retail Analytics Solutions Retail analytics has led to many beneficial advancements in the retail industry, as can be seen with:
- Delivering a personalized and profitable customer experience
As the consumer market displays a fast evolution in behavior, expectations, and new technology adoption, retail brands are searching for ways to deliver a unified customer experience across channels. Customers expect brands to give them the freedom to tailor their interactions by choosing how they want to engage with the brand. As a result, retailers are now investing in e-commerce, mobile, social media, and in-store analytics through which they can deliver an engaging experience across all channels.
Data-driven solutions like predictive analytics software use machine-learning processes to sort through large amounts of data – including available inventory, customer browsing history, and transactional information. After gathering the data, the software generates a set of product recommendations for each customer, thus generating a personalized retail experience.
- Setting effective pricing strategies.
Pricing strategies determine the fate of businesses today. Indeed, a single misstep can tank an entire enterprise. When devising pricing strategies, consider core elements such as profit margins, seasonal sales, competitor pricing, and inventory management. Retail analytics allow businesses to streamline their pricing initiatives by considering these factors and synthesizing them to help businesses determine predictive pricing strategies.
- Security against e-commerce frauds and thefts
Forget sneaky, late-night store break-ins and burglaries. Because eCommerce has taken over brick-and-mortar shops as the dominant retail mechanism, online retailers are battling new types of security threats. Online merchants need to carefully navigate security threats to protect their businesses.
They need to properly manage shoppers’ safety against credit card frauds, fake returns, spam, and other attacks on branded and personal identities and what they do with their assets (i.e., product portfolios, reputations, and everything valuable). Having a solid fraud prevention strategy is key to protecting the integrity of your business and operations against safety attacks. Technologies like ML, AI, and predictive retail analytics can help you identify new, integrated data sets – like customer behavior, transactional history, and payment options – to predict future fraud risk based on a businesses’ historical experience with customers behaving unusually.
By identifying, warning, and preventing potential frauds, companies can reduce the chances of payment failures, illegal transactions and boost sales conversions while protecting business activities. Getting started with personalization in retail A good place to start with personalization is to identify high-impact use cases. The data you collect should be both highly available and targeted and geared toward your needs in the future, particularly when new features are developed or added on.
You shouldn’t expect to create a perfect database right off the bat; rather, it’s better to continuously improve based on trials and errors, repetition and testing, then learning from those experiences. For retailers to build personalization programs with maximum efficiency, they should first form cross-functional teams that are made up of in-house analysts and IT professionals. Once the personalization program has been implemented, the retailer will want to scale it up quickly. It’s essential to work with capable partners that can guide you and help you develop your personalization program. This will build increased loyalty via differentiation in the market, more customers, and better sales performance. Ending Note
The future is now. If you’re not taking advantage of trends such as AI, big data, and advanced analytics technologies, then your competition certainly is and will continue to leave you behind the curve. Get ahead of the competition and make future-proofing your business a top priority!
