Having persuaded a potential customer to place a product in their shopping basket, it is frustrating for any marketer when that customer then abandons their order. Yet research from big data specialist Talend reveals that 40% of UK online shoppers desert their baskets at least half the time before completing a purchase.
To reduce this percentage, and increase the number who convert from intent to purchase, marketers need to know why someone would leave so close to completing a sale. The reason is not always straightforward. People fail credit checks, cannot find what they want, suffer technical problems with the website or simply feel that the final price is too high.
Others may have to go to the checkout because that is the only way to determine the delivery and payment options that are available. According to Talend’s study, many consumers use their online baskets as part of the browsing process, to generate a wish list or to help calculate costs.
Yet people can be persuaded to follow through on their intention to buy with a little encouragement. For example, 90% of respondents would complete a purchase or return to an abandoned basket if they were offered free delivery.
Marketers require reliable data to help them distinguish between window shoppers and consumers who intend to spend money.
Kieran Healey-Ryder, head of customer relationship marketing at cycle and sports retailer Wiggle, says data is crucial to help differentiate between someone’s interest in a product and real intent to purchase, irrespective of whether they get as far as the checkout. “People might read a blog about a triathlon but it does not mean they are going to take part in one and need to buy equipment,” he says.
Wiggle uses data visualisation software from Tableau to identify the right product promotion for each customer, and this sits on top of its millions of rows of customer data. The brand knows the time of day people browse its website, for instance, but Healey-Ryder says the moment someone shows intent to buy is not always the point at which they open their wallets.
“We have regular customers who view our site when commuting to London from Kent or Hampshire on weekdays, but they may not buy until the evening or weekend,” he says.
Another biking brand utilising data to get a clearer picture of when consumers are ready to buy is Evans Cycles. It uses ad tech provider Turn’s Digital Hub to pinpoint in real time where its audience is, so it can send timely reminders and persuasive offers to anyone who has demonstrated real buying commitment.
Cyclists do not purchase a new bike often but when they are in the market, it is important to reach them quickly. Evans Cycles collects data on how cyclists behave online, and whether they are regular or seasonal riders. Online marketing manager Ange Bussy-Socrate says it takes a few months for people to decide which new bike to buy.
“Data has revealed that our core audience is not always looking at cycling websites. Rather, their time online is spent on specific sections of marketplace sites such as eBay and national news sites like The Guardian,” he says. “We need to know where people are if we are to reach them when they are most likely to make their final buying decision and that includes in-store.”
For more traditional retailers, the offline experience remains important in converting sales among people who might have shown an intention to buy online.
Oak Furniture Land has more than 60 UK stores and CRM manager Sophia Greenslade says the company has a data-driven approach to targeting customers who have abandoned baskets. This includes pointing them in the direction of a nearby store so they can touch and experience the furniture in a showroom and be persuaded to buy.
The retailer analyses and segments the postcodes provided by customers during the email sign-up stage and sends out geo-targeted emails within two hours of someone leaving a shopping basket. The message includes a photo of the piece of furniture the customer was keen to buy as well as the showroom link.
This strategy, using an email marketing platform from technology company Bronto Software, boosts sales at this time of year. During November and December 2014, the retailer saw a 40% year-on-year boost in email-generated revenue. “Once the customer is in-store, it comes down to the expertise of our sales staff and their product knowledge,” says Greenslade.
Another retailer keen to make more use of data in turning purchase intent into revenue is N Brown Group, which has more than 20 clothing brands including JD Williams, Simply Be and Jacamo.
Approximately 58% of its sales are generated online, with mobile traffic seeing the fastest growth. The company has around 50 predictive models analysing intent to buy, built from online customer data that is captured using Celebrus Technologies software.
JD Williams’ head of web analytics Gareth Powell says there are different reasons why sales are not converted. Someone can fail a credit check, for example, so it is important visitors are aware of other payment options. People who abandon their baskets at checkout are emailed two days after hitting a landing page, and this has generated a 7% improvement in sales per customer contacted.
“The Celebrus data identifies the downstream customer impact of our multivariate testing, including standard cosmetic tests, from colours and placement to call-to-action buttons,” says Powell. “The data also highlights friction points on the website so we can rapidly highlight and address issues that could affect the overall customer experience and sales.” For instance, data informed JD Williams that it needed to simplify the registration and checkout pages to reduce drop-outs, so now there are fewer steps for users to go through to complete a sale.
“If a new customer abandons a purchase, we can also create a pop up saying we will email product details to them if they are in a hurry. This is important if they have arrived on our site via a search from a smartphone,” says Powell.
Many brands will send targeted emails to people who have left a website without making a purchase, and more detailed data helps ensure the right tone in all communications. Cottage holiday lettings company Snaptrip stores information about pages that people browse, then automated workflows run SQL queries against it so emails are populated with up to five of the holiday properties the consumer was browsing.
“If someone has shown intent to book a cottage in the Lake District, our email follow up might also include the best walks in the area, so it’s not a hard sell,” says marketing manager Chris Holton. “These emails are triggered at an optimised time, when consumers are statistically likely to make a purchase. Booking conversion rates for these emails are 488% above standard newsletters, and these bookings contributed 5% of total site bookings in October.”
Holidayhypermarket.co.uk is doing something similar. Marketing manager Ian Crawford says users are generally logged on across multiple devices with the same email address, and it collects user-behaviour data through a dotmailer module called Web Insights. This uses device cookies to track how people are moving around the website and connects that data to the user’s email address.
“Dotmailer collects cookie-based user behaviour data for up to a month, even if we don’t have an email address for the user. So if a customer does sign up, we’ll already know what they’ve been looking at,” says Crawford. “I’m hoping to set up logic-based triggered emails that will fire off to users according to a set of rules.”
These rules could include sending a call-to-action email with a discount if the customer has visited a landing page more than three times in a month. Most consumers will have abandoned an intended purchase at some time and brands need to understand why. However, there are now a wealth of techniques and tactics available to marketers to help them identify real purchase intent that can be successfully rekindled.
In the new age of programmatic advertising he who has data wins.
From our recent ‘State of the Industry’ report it is clear that retargeting, which is the most widely adopted form of programmatic, has gone mainstream with 88% of respondents saying it performs equal to or better than search. So how do marketers leverage this new trend? By better utilising their first-party data.
Traditional forms of advertising rely on static data to target customers, such as demographics, age and gender, but this data can be misleading. Just because I am male, 36, like plaid shirts, have facial hair and come from Brighton doesn’t mean I am a farmer.
First-party ‘intent data’ solves this problem because it targets someone only when they have shown intent to purchase. If a person browses watches on a watch retailer site, he or she is likely to be in the market to buy a watch.
Programmatic algorithms, such as AdRoll’s BidIQ, use this data to make better bidding decisions, to decide whom to show your ad to and what to bid for any impression.
In order to improve these algorithms, brands began to buy third-party data to supplement their first-party data and improve their view of the target customer. The concept of second party data, however, has not been used much in online marketing. This is where you use someone else’s first-party data to improve your own bidding decisions.
The concept of multiple companies sharing their data for the purposes of advertising is not new but it is a concept that fits perfectly into the new programmatic world. Airlines and hotels, for example, can both gain a huge amount by sharing.
Technology such as AdRoll Prospecting allows advertisers to pool their first-party customer data to create a massive set of potential customers called the AdRoll IntentMap. This pool of first-party data boasts more than 2,000 opted-in advertisers worldwide.
One brand using it is fitness wear specialist Gymshark, which wanted to drive highly qualified new users to its site. It has seen the number of new, highly engaged site visitors grow by 20%. This is a great example of a brand fully utilising the power of its first-party data.