A Survey can deliver insights, even if it doesn’t deliver a clear directive on practices. Adobe’s survey on optimization offers insights, concrete data, and some tantalizing hints—and also provokes a few important questions.
As Kevin Lindsay observed in his latest Adobe blog post, optimization investments deliver competitive advantage. To whit: 61% of Adobe Target customers have conversion rates above 2%; only 43% of companies not using Target have conversion rates are among the >2percenters. Organizations with the discipline and commitment to use Adobe Target testing and optimization capabilities get better results.
But, I have to wonder, what inhibits the success of the 39% of Target users with conversion rates stubbornly below 2 percent?
Within Adobe’s survey, among the >2percenters, 56% invest more than 6% of marketing budget on optimization, and one in five allocate more than a 25% of their market budget to optimization. But this means that 44% of the >2percenters manage to achieve their results despite spending less than 6% of their budgets on optimization.
So what’s the right amount to invest in optimization?
Based on the survey, Adobe recommends some practices:
b. Target your content
c. Make testing a priority
d. Automate and collaborate
But adherence to each one of these practices among the >2percenters ranges from 53-63%. It’s conceivable that some of the >2percenters use none of these practices. So, where are these practices the most impactful? Or, which of these practices will be the most impactful for your business?
59% of companies with conversion under 1% don’t have a test process. But this means that 41% of the <1percenters do have a test process.
Why are so many testing efforts failing to improve conversion? That’s really 3 questions:
1. What are the ineffective testing practices?
2. Why are ineffective practices so widespread?
3. How long does it take – what is the roadmap – for testing to begin to improve conversion?
I wish I had the data that would answer these questions. It is clear that the discipline of digital marketing has a lot to learn. We have a long way to go on improving – optimizing – digital marketing. Adobe invests significantly in illuminating the path. Thanks for that.
One of the many fantastic benefits of the digital age is staying connected with your life, even when far from home.
Geotargeting is a deadly assault on that promise of a digital home-away-from-home.
I’ve been travelling for some months, and feeling frustrated.
When I ask for [whatever].com, I want the .com version. Not the local version.
Skype won’t let me renew my subscriptions. The services I use aren’t offered in Paris, where I am today.
We all know that Netflix, Amazon and others won’t let us stream our subscriptions outside the U.S.
Google searches deliver ads for Portuguese businesses, in Portuguese. I left Portugal on April 30. I can’t really read the ads, but it doesn’t matter. I’m gone.
Amazon suggests that if I’m shopping in France, I’d probably like to cliquez ici to get French items and prices. But if I do, I must shop in French. And ask Google to translate the pages! Well, the results are often amusing, so not a total loss, I’ll concede that point.
Google always mentions that it is available in Portuguès, Français, or catala, etc.
This despite the fact that I am logged in to Google. Google, I have been using you for a decade. Have you not noticed that I am an English-only sort of guy? Despite having been in Paris for 2 hours, I am not yet fluent in French.
Skype, we have had a [monetary!] relationship for years. Doesn’t that count for anything?
Amazon and Netflix, can’t you find a way around your licensing problem? If you could, I would restart my subscriptions to your excellent services.
A few of my favorite sites manage to continue to do business with me as if I am still the American I was a few months ago. WordPress. Facebook. NYTimes. Customers.com. Voguepatterns.
Do your geo-targeting tactics ignore your customers’ identities, and undermine your customer relationships?
I am not talking about Reporting: canned reports are about as appealing as canned spinach. I am talking about communicating with all staff impacted by recommendations. The solutions typically have a dashboard to communicate the information the vendor thinks is important, presented as… online, navigable reports.
No dashboard has just the information you need. You get some of what you need, plus a lot of unneeded or unintelligible information.
There are three types of information that must be clearly communicated for you to be successful with recommendations:
1. Achievements. The value of all, some, or one project, in terms congruent with the project’s goal. If the goal was to increase revenue for a category or segment, the achievement should be expressed in terms of revenue.
2. Opportunities. Some projects (or campaigns, or pages, or customer segments, or sites) are performing really well. The solution should observe that there are similar projects not performing well, and let you know the potential value of improving them.
3. Goals and status. Programs, projects and tests have goals, and status should be reported against those goals.
The Dashboard (the solution’s reporting interface) is only one element of communications. It must be very easy to prepare personalized communications for stakeholders around the organization. Executives and project managers need different information; teams only want to see information about their projects or programs; executives may want summaries about the projects in their portfolio. Project and program managers should be able to set a few rules about who sees what, and have reminders automatically sent to people to come take a look at their personal dashboard page.
Without the personal dashboard page, people don’t see all the information they need; they are confused and distracted by information that is not relevant; and they can’t get the summaries and comparisons that are meaningful to them. Lots of mental arithmetic. Lots of phone calls begging for explanations of the irrelevant data. Lots of frustration, and really, let’s not bother to ever look at the “reports” again.
I haven’t found a solution provider that delivers #2. They all sort of deliver #1 and 2, but not with a personalization capability that makes communication effective.
On June 4, 2013 Salesforce.com announced that it has signed an agreement to acquire ExactTarget.
Salesforce.com has been the dominant CRM platform for a decade. It should be the leader in all aspects of customer interaction. But in recent years, it has fallen behind in supporting marketing activities that are increasingly customer-focused, rather than segment-focused. Marketing has gone digital and targeted.
Saleforce.com’s forays into social marketing solutions only partly fill the gap. What has been missing is the tools to create and execute personalized (targeted) campaigns.
Acquiring ExactTarget is a step in closing more of the gap.
If recommendations are so important, why don’t companies invest in making them more effective?
In a recent survey by Adobe, 40% of ecommerce respondents have automated recom-mendations on their sites, or are in the throes of implementing them. Roughly 1 in 5 media and financial services companies are investing in automated recommendations.
Yet, half of companies don’t do anything to optimize the recommendations. And a quarter “optimize” by manually updating recommendations. Behavioral, profile, and business rules are each used by just under 20% of respondents to improve recommendations. The takeaway for me is, 75% of companies aren’t committed to making sure a visitor sees the best selection of available content.
Why would a company invest in a technology that has demonstrable bottom line impact, but not tailor it to produce the best possible results?
a. Mediocre (not optimized) is good enough
b. Testing is too hard
c. We don’t believe testing is a good investment, or we don’t understand it
d. We don’t have the resources (yet) – either skills, budget, or technology
I think the answer is a combination of a and b. Once automated recommendations are deployed, most companies get at least mediocre results from them. Recommendations service providers do a pretty good job of automatically optimizing recommendations, and guiding their clients to deploy good (best?) practices. Testing to optimize recommendations requires investing in an ongoing program, requiring specialized and scarce skills. The path of least resistance = set and forget.
A decade ago I watched vendors of site search solutions teach customers new skills: how to manage synonyms and common misspellings, how to identify poorly performing searches and improve them, how to change their content to produce better search results, how to create and manage rules to control search, how to offer the right navigation facets…
The list was long and daunting, and if you wanted to have great site search, you had to invest in these unappealing skills. Most sites did not invest enough to have great search, but managed to cobble together enough resource to have half-way decent search. Hence the unchanging verdict from users, “Search sucks.”
We’re all pretty excited about recommendations these days. Users like them because they are easy to use and often interesting. Web site managers like them because you can achieve “half-way decent” with very little effort, delivering a nice bump to your bottom line.
But if you’re not content with “half-way decent,” what are the tasks to achieve great results with recommendations? Most likely, you’ll be making a study of best practices, so you can set up recommendations well. Then you are going to test and improve them, iteratively, by finding poor performing segments, pages and categories and trying out different approaches. And this last task will never end, because there are always poor performers; your items change; trends, fashions, and markets change; and your visitors change.
But how much should you have to invest in this improvement program? I think recommendation solutions can do much, much more of the heavy lifting than they do today. Recommendation engines are prediction engines. They predict, using a variety of methods, the items that a person is most likely to find interesting. The vendors tout the expertise of their staff in advising you on how to deploy and improve recommendations. They claim to know best practices.
With all this technology and expertise, shouldn’t the recommendation solution deploy best practices automatically? Tweaking the best-practice default would be a lot easier than learning best practices and applying them. Shouldn’t its analysis and modeling identify poor performing areas, and predict actions that will best fix them?
Here are three questions that you should ask any vendor whose solution you are considering:
1. In what ways does the solution implement best practices as a default, selecting locations, recommendation strategies, and algorithms?
2. In what ways can the solution automatically optimize recommendation approaches to reach client-specified business goals?
3. How does the solution identify and prioritize opportunities for improving recommendations?
Personalization is a concept open to wide interpretation – its meaning seems to be, well, personal. One man’s integrated marketing is another man’s personalized customer experience. But at heart, personalization is about persons. And this is why the customer profile is, or should be, at the heart of anything calling itself a personalization solution.
Here are four questions you can use to evaluate the customer profile capabilities of any personalization solution.
1. What data does the solution contribute to your customer profiles? For example, does it create segments based on customer context or actions or social data? Is the profile persistent across sessions and devices?
2. How is data created by the solution made accessible to other systems? or, Where and how is solution’s customer profile data stored? For example, if the solution models customer behavior to make predictions that are potentially useful for marketing campaigns, marketing automation tools need access to that data.
3. What customer data is used by the solution? Does it use current behavior, past behavior, order history, personal preferences, segmentation, third party data, social network data? Is the solution designed to accept and use any data you feel is useful?
4. What control does the solution give to customers over the use of their profile? Can they opt out of tracking, or constrain the use of their social data, or request anonymity at times?