Driving Personalization through Marketing and A/B Testing
This article was written by our partner REO, as part of our series highlighting direct insights from our large ecosystem of partners.
In 2019, for the first time ever, digital ad spend represented more than 50% of total global marketing spend. Whilst the UK was considerably ahead of this trend (63.8% of UK’s total ad spend was attributed to digital in 2018, 66.4% in 2019), the US has now joined the group with online ad spend going from 48.6% in 2018 to 54.2% in 2019. With eMarketer forecasting a 17.6% year-on-year growth (to $333.25M) in worldwide digital marketing spend, the need to ensure each of your marketing channels is delivering the best possible ROI has never been higher.
Within the conversion rate optimization (CRO) space, most brands conduct A/B testing without fully considering which marketing channel or source their customers have come from. Customers are typically bucketed into various user segments based on their purchase history, onsite behavior, geographic and demographic data. However, users within the same audience segment can often demonstrate varying behavioral attributes when navigating through the purchase funnel, across countless online and offline touchpoints.
Let’s Take Paid Search as An Example
If a user arrives on your website via paid search, you already know what they searched for and which ad they clicked on; however, users who click on the same ad, but searched for different terms/items, will often experience the same customer journey. For instance, if a customer has searched for “luxury men’s white shirt” – not only do you know the item they are looking for, you also know they are looking at the higher end of the market.
A/B Testing the landing page a user is taken to is quite common, but you can go a step further and explore how to change the experience for the customer based on their search criteria.
A potential testing idea could involve pre-sorting these shirts by highest price first, and on the Product Listing Page (PLP), displaying all the available men’s white shirts. This can develop into personalization if the user has visited the site previously, within the cookie period; e.g. by storing size data within the cookie, you could pre-select the shirt size which the user filtered by on their previous visit.
Reducing the number of clicks and filters it takes a user to find their item can only have a positive impact on conversion rate, especially on mobile. So, by showing a customer the items they’re looking for, sorted by their desired price point and filtered by their size, you will make the purchase journey more tailored to that specific customer.
Understanding a visitor’s context (location, date and time of day, device, internet connection, etc) as well as their intent (are they here to complete a quick purchase, to research and compare products, to seek inspiration, to test a coupon, etc) add an invaluable layer of behavioral understanding to your analysis, and will allow you to execute a more impactful form of personalization.
Making the Affiliation between A/B Testing and Voucher/Cashback Partners
By applying this testing method to the affiliate channel, you can optimize the largest click and revenue drivers; namely voucher and cashback websites. After all, you can already assume that users coming from these two affiliate types are both online-savvy and price-sensitive.
Voucher and discount websites should have a conversion rate of at least 20-25% on mature affiliate programs – so any of these affiliates who have a conversion rate lower than that, represents an opportunity for incremental revenue. For cashback sites, expect this figure to be upwards of 40%.
A test idea for these two affiliate types could be to re-enforce the discount or cashback offer listed on the affiliates’ website. For instance, if the deal was “Save £15 when you spend over £100” – you could use a “loading bar” at the top of the page which gradually fills up as you add items to your basket, until the user hits the spend threshold to activate the discount.
For cashback sites, you could test a cashback calculator onsite, which automatically calculates the amount of cashback the user will earn if they purchase everything currently in their basket. This type of gamification can be incredibly effective in increasing the number of units per sale and, in turn, the average order value.
Serve Less Content, but More Dynamically
“Content is King” – we’ve all heard it before, but how can you be smarter in how you serve it? Content, and specifically dynamic content, is another channel where source-based A/B testing can improve engagement, click-through-rates and leads/ sales. If you know the article or blog post a user has come from, you can use this insight to serve them relevant and dynamic content, making their customer journey more seamless and less detached across the two sites.
User journey analysis shows that visits to content sites usually happen in the “Discovery Phase” of the sales funnel – including on product review sites, influencer social posts, news/magazine sites and blogs. Such content is informative and persuasive; perfect to push the user towards the bottom of the funnel.
Some of the more content-heavy merchants, such as insurance brands or high-end technology retailers, will have an eclectic and extensive array of content across their website, making navigation more muddled. A solution? Reducing the amount of content on-site and instead, storing the less frequently visited content pages elsewhere, to then be served dynamically.
For example, if a user looking to buy insurance is reading up on excess and the impacts it has on a claim and future premiums, the existing content about excess could be tweaked accordingly – which could be as simple as changing the title of an article, calling out the keywords or changing the order of the content on that page.
Again, a granular analysis of how customers are interacting with individual elements of content will help paint the complete picture of engagement. Measuring clicks alone will only tell one part of the customer behavior story: tracking metrics such as exposure, attractiveness and conversion rate per click (to name a few) will give a more complete view of how content is contributing to (or stalling) the user journey.
As the capabilities of A/B testing and personalization platforms continue to evolve, the way you test and analyze a customer journey should follow suit. One of the major challenges of channel/source-specific testing can be a lack of traffic volume. If you have insufficient traffic, it will take a while before a test reaches significance. For example, the 5th highest paid search term, or 4th largest voucher site probably won’t have the volume to justify running an A/B Test on.
Want to Know More?
Contact us! REO is a digital experience agency. We are an eclectic mix of bright and creative thinkers, embracing the best of research, strategy, design and experimentation to solve our clients’ toughest challenges. We work across a variety of sectors, with companies such as Amazon, M&S, Tesco and Samsung.
Also invaluable to our company is our scope of partners, including Contentsquare, which allows our customers to capture the nuances of their end users’ behavior for even more sophisticated segmentation and ultimately, deeper personalization.
Whatever the challenge may be, REO applies design thinking to identify and deliver big growth opportunities.
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