Technology-enabled marketing has changed the game for marketing leaders. CMOs are increasingly held accountable for their portion of executive responsibilities, and that means more activities tied to reporting and analytics.
To date, there has been more than $21.8 billion in tracked funding for marketing tech companies according to Scott Brinker’s marketing technology landscape and VBProfiles. Of that $11.84 billion was invested in marketing experience tools and $5.84 billion in the marketing operations space. In other words, elevating customer experiences is the primary focus followed by investment in the solutions that advance and automate those experiences.
So why, with all this cash flying around, are marketers still having a difficult time harnessing these tools? A recent SimpleRelevance study analyzing 418 marketing emails from 20 top Internet retailers proves retail marketers aren’t properly leveraging marketing technology to improve experiences and operations.
The tried and true digital marketing tactic – email – is a simple and transparent source of customer engagement data for marketers. Nearly every email platform offers insight into opens, clicks, and conversions, supplying ample amounts of data to drive future email marketing decisions. This insight enables testing of subject lines, content, images, and more allowing marketers the opportunity to send their customers ever more personalized offers. So why is it that top retailers fail to execute even basic gender segmentation?
For example, in our study, Macy’s sent the exact same product recommendations to both male and female customers including eight categories of products – five directed at females, two unisex, and one directed at males – making it evident that they did not leverage purchase or profile data in their email campaign.
While the email Macy’s sent was complex, including designed content, multiple product offers and discount offers, they didn’t take their market a step further and incorporate technology to optimize each element of the email. Offering personalized emails is proven to generate revenue per email that is more than six times higher than non-personalized emails.
The first step in offering a truly personalized experience is leveraging available data. According to IBM, CMOs list an inability to leverage data properly as a top concern. We’ve seen firsthand the difficulties marketing executives experience when attempting to tie multiple data sources together to create a comprehensive customer profile. Data formats aren’t standard, some data is outdated, and normalizing it all takes a unique combination of advanced technology and human knowhow. Too often this barrier is enough to prevent marketers from leveraging the full set of data assets available.
Layer with that the complexities of data ownership – who is in charge of customer data versus purchase data versus social data – which could be the CIO, CTO or CMO depending on the organizational structure, and the task becomes nearly insurmountable.
This is where marketing operations technology investment plays a key role. In the past five years, computing power has skyrocketed to new levels, data manipulation software has advanced, and API access has enabled ease of data exchange. While “it’s too hard” may have been a viable excuse five years ago, it no longer cuts it.
At the basic level, product and transaction data can be easily leveraged to offer personalized email messages. However, in our study we found that only 9% of retailers offered personalized product recommendations, and a quarter of the retailers analyzed failed to offer products recommendations at all. Worse yet, some retailers claimed recommendations were personalized when, upon deeper analysis, no data was used to identify customer A from customer B.
Predictive analytics is the technology needed to execute the individual content recommendations retailers claim to provide their customers. It’s the bridge between marketing operations and marketing experiences.
Leveraging collected data from customer actions (opens, clicks, purchases, website engagement, social media, etc.) combined with product inventory data (items, sizes, fabrics, etc.) to offer customers unique recommendations transforms historical engagement data into an increased future purchases. This requires using a variety of predictive models and algorithms to solve each retailer’s unique business problem.
For instance, the recommendations can optimize on inventory movement, increased sales of similar items, increased sales of complementary items, or any number of sales and marketing objectives.
These technology solutions are complex, making marketing leaders’ decisions increasingly difficult. While the investment in marketing technology continues to increase, it’s clear there is still a significant learning curve before for marketing executives will be able to offer a truly personalized customer experience.
The ability to utilize advanced technology to evolve common marketing strategies such as email will prove which marketers are first movers, earning double digit revenue growth, and which will be left behind.
Erik Severinghaus is Founder and CEO of SimpleRelevance.