In the last few years, retailers have accelerated efforts to truly understand their customers. Knowing preferences on an individual level, including what they like, don’t like, and might like, has let even large, global stores make better connections with their shoppers.
And whether they are consciously aware of it or not, customers respond positively to this level of personalization; 76% expect businesses to know their needs and expectations. This shift in outlook has pushed businesses to improve methods of reaching consumers using data.
Luckily, retail analytics software is more accessible than ever. The retail analytics market is predicted to be worth $23.8 billion by 2028, and retailers can use it to tailor the shopping experience using objective, measurable data.
As retail analytics evolves, opportunities for personalization will only get more exciting. Here are our top predictions for what 2022 will hold.
Many retailers need to go back to basics when managing customer data.
Your data from POS systems, e-commerce, and any other customer touchpoints should be stored in one place. And it should be accessible through your retail marketing analytics software. Importantly, these data should be accurate and updated in real-time. This provides you with a single source of truth, empowering you to better understand and segment customers.
Going back to basics, improving, and refining your customer data quality is vital to remain competitive. And the capabilities of retail analytics is growing. Techniques, like predictive models, that allow retailers to anticipate what customers might purchase, have become increasingly accurate. But they need equally accurate data to work well.
If you want to keep pace with the latest innovations and implement them in your business, you need better quality data. Otherwise, you’ll quickly fall behind your competitors, who might already be taking advantage of them.
Knowing who your customer is, no matter how or where they interact with your business, will be important in 2022. Whether a customer only shops in-store or is a frequent buyer both online and in-person, you need to link up their transaction data. By doing so, you’ll maintain an accurate, historical record of every payment transaction for each customer.
When you can identify customers across multiple channels, you’ll find more ways to reach them or upsell your products. There are a few ways to do this. Firstly, by using a payment token that recognizes the card a customer uses in a transaction. If they’re using the same card across different channels, retailers can understand that it’s the same person. Secondly, using a loyalty card scheme that’s enabled across each customer channel. Or thirdly, by using coupon marketing on your receipts. With receipts, customers can be reached without necessarily ‘identifying’ them.
The proportion of shoppers who engage with loyalty programs has gone down. 79% say they’re not as interested in accumulating points anymore and would prefer immediate benefits to maintain their loyalty. Meanwhile, 68% say achieving their loyalty is more difficult than ever before.
For retailers, this raises questions over how accurately they can track consumer purchases. Even if a large proportion of your customers are signed up to your loyalty scheme, how do you make sure they keep using it, or capture new customers?
To overcome this, you can use an alternative such as a payment token. At the payment transaction stage, a payment token records customer information like their payment card type and the last four digits of their card. When they use this same card again, the payment token lets your retail analytics software know it’s the same card being used. You can identify transactions, repeat purchases, and more, all without their personal information.
Several retailers are starting to develop their in-house retail data science departments and capabilities. Previously, large retailers might have relied on consultants to manage this for them. But many see the benefits of building their knowledge in-house.
The power of data has become more prominent in the last ten years. When internal teams can quickly and efficiently grow their data science functions from the inside, it lets retailers remain agile, adapt quickly to changes, and implement new ideas faster.
Retailers will be hiring marketing experts that can harness data and marketing functions together. However, while building the skills internally is a good move, retailers do not have their own analytics tools. These can be costly and time-consuming to develop and maintain. Pre-existing solutions like retail data analytics software can relieve the pressure on internal teams, and enable retailers to scale their analytics across their business.
With this, retailers develop their data expertise but rely on best-in-class software to unlock powerful results.
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