Why are retailers spending more money for the same number of eyeballs?
Columnist Liad Agmon explains how retailers can use data to personalize and optimize their campaigns for better returns.
The e-commerce industry spends more on digital advertising than on any other vertical. By the end of 2016, digital ad spend will total approximately $15.09 billion. That figure will likely climb to $23.04 billion by 2020.
These numbers are staggeringly high for a reason: an investment in digital advertising pays off. It can drive up to 51 percent of sales, as it did for Williams-Sonoma in 2015.
The battle for consumer attention
Even though digital advertising works, it has become increasingly difficult for retailers to experience growth commensurate with their investment in paid media campaigns. The competition for consumer attention has never been fiercer than it is right now. And the saturation of the market has meant that advertising costs have risen dramatically.
A recent report from TVTY reveals that 93 percent of marketers say that they’ve had to spend more in order to merely maintain their traffic and audience levels due to rising acquisition costs. To cope, some retailers are cutting budgets and reducing the number of digital campaigns and paid media channels. Others have done the exact opposite — increasing their spend in advertising campaigns just to maintain their current traffic.
Most companies disproportionately focus on acquisition, often at the expense of conversion. Some pundits have indicated that “for every $92 spent acquiring customers, only $1 is spent converting them,” which, while a bit outdated, still holds true for many organizations.
But retailers know they must do more than simply get customers through their digital front door. They have to focus on maximizing the yield for their traffic, earning the biggest bang for their buck. The best way they can achieve this is to utilize the insights and data they get from those visits in order to personalize customer experiences, which will increase the likelihood of conversion. By improving customer experiences, retailers can increase online revenue without spending more on traffic acquisition.
Employing data to maximize investment
The simplest way to do this is to focus on sending traffic to the best destination on the site. Rather than sending every user from a specific campaign to the same landing page, retailers can experiment with different page destinations and content variations, depending on the context, past behavior, geolocation, local weather forecast, or other available data. This is not a game of Monopoly — not everyone has to pass “Go.” As more information is collected with each subsequent visitor, the traffic is redirected to the pages that generate the highest post-click revenue.
For example, if a consumer searches on Google for “jeans,” there is little benefit in sending them to the generic jeans-category page. Retailers probably know more about them than the simple fact that they’re interested in jeans.
For example, they can utilize anonymous gender data to differentiate between jeans for males or females and show the visitor the gender-appropriate page for their jeans search. Or, for returning visitors, retailers can use historical data to change the default sorting order of the items for purchase.
If, for instance, the customer had previously browsed sale items, it’s possible to tweak the sorting order in real time to show them jeans priced from low to high. These are just a couple of ways machine learning can be leveraged to customize landing pages and improve chances of conversion even if the customer’s search was not highly detailed or leading.
Of course, some consumers will reveal more specific purchase-intent clues, such as searching for “black skinny jeans under $50.” With this additional information, retailers can adjust landing pages and site presentation accordingly. For example, the user’s stated desire for black jeans under $50 might point to a preference for black clothing and price sensitivity. Armed with this educated guess, the site can recommend other black or dark colored clothing with a default sorting order of items under $50, or simply sorted by price, low to high.
Even more personalization is possible if the user is a returning customer or has a registered account. The retailer can remind them of past purchases or use other information on preferences to further customize the experience.
As traffic acquisition costs increase, retailers have a choice to make: they can either spend more to drive more traffic to their store or spend the same but improve the yield of that advertising. Put this way, the decision sounds like a no-brainer. By employing data and personalization strategies, retailers can achieve a 15 to 20 percent uplift in revenue generated from their campaigns, as some cases have indicated. Getting the consumers to your site is how you win the battle; getting them to convert is how you win the war.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
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