Who is second to know that you are pregnant? Google.
The moment mums-to-be start searching for pregnancy-related information on Google, retailers get a signal that someone is starting their journey to parenthood and can begin to target them accordingly. For retailers or brands with a Point of Market Entry (POME) opportunity, who are looking to talk to new families, this is critical – if you make your first parenting purchase with them, you’re likely to stick with them throughout your pregnancy and as your family grows. It also helps to minimise wastage and increase engagement, using big data to ensure only relevant communications are received by customers.
For retailers, big data can be a game-changer. It can help you understand what customers really want, a critical success factor in today’s increasingly tough trading environment. The depth and intimacy of understanding consumers on this scale is new and powerful. Although mass information like this is hard to handle, if harnessed with the right tools and processes, retailers can interact and answer customers in a way that is superior to their competitors.
We are also starting to see retailers like Shop Direct implement big data on a huge scale and deliver astonishing results. Driven by machine learning, Shop Direct is now personalising more customer touchpoints than ever before, from customer emails and off-site digital advertising, to homepage content, navigational menus and product recommendations. This is particularly impactful in categories like consumer electronics where purchases made on ‘impulse’ are rare. In fact, there are clear signals that someone might be interested in a new device, including browsing data, purchase history and product lifecycles. If you can successfully contact customers who might soon enter the market for a new device, you have the chance to win versus competitors.
Big data also doesn’t have to be limited to customer data. ASOS has cleverly used its own mass-scale product data to allow shoppers to upload a picture of an outfit they have seen and then present them with the closest options from the retailer’s stock. This enhances the customer experience in a different way by speeding up the search process, taking a customer from an offsite outfit image to potential purchase in just one step.
However, there are some watch outs. If big data unlocks insights that are truly predictive, i.e. Customer A bought product B and therefore next will be product C, this can, of course, be very powerful. But there’s also a danger that when it is done incorrectly, it becomes self-fulfilling. For example, when moving into a new flat, I bought a light from Amazon. For the next six months, they only targeted me with lights when in fact, I had already fulfilled this need as a customer. If they could instead have used their on-site traffic data to put me into a ‘new home’ customer segment, they’d have had the opportunity to sell multiple new products from DIY tools and kitchenware, to home furnishings.
While predictive algorithms based on purchase data are effective from a media targeting point of view, there is also a risk that they can lead to an ever-decreasing pool of potential customers. Retailers and brands need to ensure they combine this approach with more traditional mass media if they are to gain bigger brand growth from a broader audience.
Big data has valuable potential for brands who need this data to win at the point of purchase. By partnering with retailers to unlock insights like this, they can reach customers lower down the purchase funnel with the right content and increase their conversion to purchase. For example, for a washing machine manufacturer whose customers often only buy when their previous machine breaks down or when they move home, this kind of big data from retailers can uncover a whole new way to market to customers than was previously possible.
Procter and Gamble’s (P&G) chief brand officer Marc Pritchard has discussed the need for ‘mass one-on-one’ marketing as the next phase of the big data revolution. Rather than using digital advertising to reach customers on a large scale cheaply, he believes all activity must be viewed through the lens of the shopper. Amazon and P&G have partnered to ensure all activity is delivered when the customer is ready to buy – and it appears to be delivering results for them. The improved targeting is increasing ROI by four times, as well as offering a better shopping experience for customers.
Shoppers are now using multiple interaction points in the path to purchase – mobile, social media, e-commerce sites, search engines, stores and more. To truly be insightful and therefore influential in the path to purchase, retailers must ensure they are collecting data from all channels possible and connecting it all together. Only then is it possible to fully understand how their customers are shopping, allowing retailers to use that data to deliver an enhanced, progressive customer journey. Ultimately, it is important for retailers to harness data they have access to and use it in the right way, at the right moment, to get the right outcome from the shopper.