How Is Retail Analytics Improving In-Store Customer Experiences?
The customer is King.
Royalty commands, even expects, princely treatment. At all times.
You're no stranger to the royal treatment in the retail world, equaling the customization of products and services based on the personal preferences of customers. What's interesting, though, is how critical it has become for retail businesses, such as yours, to gain the all-important competitive edge by leaving no stone unturned in rolling out the red carpet for customers and delighting them.
What, then, is the one thing you can do to ensure that you gain and sustain a competitive advantage? Acquiring a holistic understanding of who your customers are—knowing their likes and dislikes, preferences, and the factors driving those preferences—is the reasonable way forward. But this, you already know.The challenge often lies in how to access this information. Deploying a customer analytics solution from a leading services company like Syvylyze Analytics , which offers tailored data management and analytics solutions for retail and marketing intelligence, will provide you with access to these insights.
How Is Data Analytics Improving Consumer Analysis and Segmentation?
You will agree when we say that a key step towards achieving retail success is knowing one's customer. Retail analytics leverages data delivered by machine learning-driven advanced algorithms to accurately identify and enhance customer segmentation. Simply put, customer demographics, preferences, and buying behaviour (recency, frequency, and the monetary value of purchases) are analysed to identify patterns, helping create more accurate and targeted segments based on:
Demographic factors encompass various aspects such as age, gender, educational background, ethnic heritage, income levels, employment status, and individual interests.
Recency, Frequency, and Monetary metrics involve the timing of the most recent transaction, how often a customer engages in transactions, and the overall value of those transactions.
Behavioural elements encompass past purchasing patterns, brand preferences, significant life events, and other actions or preferences displayed by the customer.
Psychographic aspects delve into an individual's beliefs, personality traits, lifestyle choices, personal interests, motivations, and their set of priorities.
Geographical indicators include country of residence, zip code, prevailing climatic conditions, differentiation between urban and rural areas, and the accessibility to various markets within specific locations.
This enhanced customer segmentation is characterised by its ability to upgrade personalised marketing strategies by tailoring promotions and recommendations for specific customer cohorts . Real-time customer data analytics also enable dynamic adjustments, ensuring relevance and engagement. When customer segments are understood in detail, inventory management, pricing strategies, and customer experiences improve, ultimately fostering loyalty and maximising revenue.
A modern retail analytics program, therefore, can be invaluable in providing insights into:
o Changing consumer preferences (includes customer behaviour prediction)
o Store layouts that entice customers
o Supply chain disruptions
How Is Customer Data Utilisation Elevating Customer Experience?
Speaking of store layouts, a February 2023 Forbes article states that “As consumers show more of an interest in shopping in-person, there is an increased need to make the shopping experience as seamless as possible. As a result, brick-and-mortar retailers are investing more in retail technology tools and platforms to ensure steady revenue flow and better store management.”
On that note, did you know that adult consumers have been reported to be shopping less online in 2023 (29.9%) than they were in 2021 (46.5%)? The difference now is that they are expecting the online digital experience to be merged into their physical one.
In light of this critical-for-retailers-to-know customer preference, what can be done to improve the in-store shopping experience? The answer lies in incorporating those elements of online shopping that consumers love into on-the-floor setups – Fast, frictionless checkouts and ensuring product availability at all times.
How to do both? – Deploy an analytics solution that extracts information on merchandise performance from tags attached to products. Information that includes:
o What customers are seeing
o Whether (or not) customers are engaging with a product, purchasing it, or abandoning it
o What is selling well individually and as a bundle
o Whether (or not) a product is available and in what quantity
o Which shelves or locations in the store are grabbing the most attention
When integrated, this information helps guarantee appropriate product availability, placement, and quantity. Retailers can enhance the purchasing process, safeguarding profit margins and turnover rates. In fact, the insights gained at the product level can be extended throughout the entire retail network, swiftly improving merchandise effectiveness. In-store observations can be leveraged to optimise and customise stores, contributing to overall operational efficiency.
Speaking of shoppers' experience, we just had to share this with you – a Deloitte survey reveals that 20% of consumers are open to paying a 20% premium for personalised goods or services; a discovery that aligns with the current trend among brands which are increasingly customising their products to establish stronger trust with their clientele. Hyper-personalization ahoy!
In conclusion
Customer analysis tools are increasingly becoming go-to solutions for retailers seeking to consolidate information from isolated datasets, including crucial external data on demographics, psychographics, and share of wallet. The ultimate objective is to gain a thorough understanding of how best to personalise offerings/services to meet customer preferences, leading to a better overall customer experience that promotes satisfaction, delight, and loyalty.
