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GUEST COMMENT A little personalisation goes a long way

Scott Logie is customer engagement director at Sagacity, and chair of the customer engagement committee at the Data and Marketing Association

Retailers have had a rough few years, with more on the way. Coming out the other side of the pandemic, businesses are faced with renewed economic challenges. Interest rates are at their highest level since the 2008 financial crash, driving additional costs throughout the supply chain and shrinking margins. The ensuing cost of living crisis means the Consumer Confidence Index (CCI) has dropped six points in the last year to one of its lowest recorded levels since measures began. Ultimately, fewer people have cash to splash, so every pound of retail investment must deliver a return.

For decades, brands have searched for the perfect formula to boost the golden trio of engagement, loyalty, and sales. It often comes down to one thing: personalisation. The more communications, products, and services are tailored and relevant to a specific audience or individual, the likelier they are to buy. Yet while everyone is talking about personalisation, even the savviest brands still struggle to operationalise it at scale. But it doesn’t need to be all or nothing – a little personalisation can go a long way. Retailers don’t need to hyper-personalise to build meaningful relationships with their customers. 

Step one: Filling in the data gaps to gain a single customer view

You can’t personalise if you don’t know who your customers are. So, as a first step, retailers need to ensure the information they hold on customers is correct and complete. This isn’t always straightforward. Some retailers hold information on millions of customers and potentially hundreds of products; the sheer volume can be overwhelming. Others may have a niche customer base but lack internal resource and processes to ensure that data is being captured in an effective and consistent way. 

Whichever camp you fall into, cleansing data and having a clear strategy around how data is continuously maintained is critical. Sometimes one customer may have several accounts, so it’s key to link all of these to make sure all the information on that customer is in one place. Doing so will give you a single customer view of all purchases and returns linked to their most current personal information – such as address, email, and phone number. This is also critical for compliance with GDPR regulations. 

Step two: Getting a foot on the personalisation ladder through segmentation

Now customer data is complete and accurate, you can start to look for some top-level patterns that will help you group, categorise and segment customers into targeted sections. This step can feel overwhelming, but the important thing is to only bite off what you can chew. There is no point in having 100 different segments if you do not have the capacity to tailor communications for them all. Instead, start off by focusing on a handful of broad categories your team can comfortably manage. This approach can make a big difference. For example, Sagacity worked with card and gift retailer Moonpig on a segmentation project, which saw the company gain a 50% increased revenue share by creating just five different personas.

The Digital Marketing Association (DMA) found that 51% of consumers agree they often change their mind about what brands to use as a result of deals, so getting those timely offers in can really make a difference. This could be as simple as identifying a group that buy children’s clothes and sending them offers around ‘back to school’ products. Or sending follow up offers after someone has bought a tent for other types of camping gear – like a sleeping bag or roll mats. It could be as basic as making sure you split by gender or age. The key thing is to ensure that any segmentation you do is sustainable so that communications can be managed in the right way.

Step three: Taking a value-driven approach

Once the foundations are in place, you can really start to fly, adding further datapoints to give even richer customer insights. For instance, tracking which channels customers tend to buy through, the types of incentives they have clicked on, and how often they make purchases. This analysis could be even further enhanced by third-party data sources to establish a customer’s household composition, other services they have purchased elsewhere, even what their hobbies and interests are.

By layering analytics and machine-learning techniques on top of that data, you can build a picture of the value that each customer brings. For example, a clothing retailer might find customers aged 18-25 purchase more items, but return over half of their haul, increasing their cost to serve. In contrast, the next age bracket may only be buying one or two items but spend more and return less. Encouraging the latter group to buy more may therefore be more profitable. 

Taking a value-driven approach can inform micro-segmentation of the most profitable customers, so retailers can invest in hyper-personalisation to retain them, whilst reducing marketing spend on less profitable ones. Additionally, if retailers can understand the characteristics of their most valuable customer profile, they can use these insights in prospecting to acquire new customers that match their profile.  

A little goes a long way 

Personalisation doesn’t have to be all or nothing. There’s no need for retailers to have millions of iterations of materials for every customer. Instead, it’s more impactful to focus on retaining and prospecting the customer profiles that can bring the most profits to the business. Making such informed decisions just isn’t possible without quality data. In the long run, it’s good data strategy that will enable retailers to hold a competitive advantage, gain a larger market share, and reap a return on personalisation investment. 

Scott Logie is customer engagement director at Sagacity, and chair of the customer engagement committee at Data and Marketing Association.

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