Customer Value vs. Valued

Customer Valued?

In our age of automation and AI, marketers risk becoming detached from how their business practices, products, and services are perceived by their customers. Marketers have it all wrong by focusing solely on customer satisfaction or a willingness to recommend: these measures do not capture the customer’s relationship-based perspective. Customer satisfaction or willingness to recommend are thin, shallow, “last click” based, and largely transactionally-oriented.

The customer can be momentarily satisfied with their last transaction (product or service): it delivered a promised benefit. But that is, by definition a transactional experience, and often devoid of any emotional richness.

In working through these distinctions with our clients, the notion of customer value (or customer lifetime value, aka “CLV”) can be the polar opposite of whether the customer feels valued.

From a financial or marketing perspective, a company’s approach to customer value is formulaic: extract maximum value from the customer. The metrics take many forms: ROI, ROAS, narrowly targeting, and upselling to name a few. But this “share of requirements” approach (i.e., siphoning off more revenue from the same customer) is entirely transactionally driven. There is no “emotional stickiness” created with the customer from a single transaction, or even from multiple successful transactions.

From the customer’s perspective, feeling valued creates an emotional connection which is deeply internalized and an incredibly powerful sticky magnet for retention.

As a CMO, how would you answer this question from the customer: “Does this company appreciate my business?” If you don’t know, consider path-to-purchase or other in-depth insights work. Test new programs. Test reward structures. In what ways can your company demonstrate to the customer that their business matters – that they themselves are valued?

Even highly satisfied customers can be quickly dislodged by a competitor who can, for example:

  • Offer products at a lower price
  • Offer products with more features/benefits
  • Offer products that appeals to more users
  • Offer products that have more use cases
  • Deliver products in less time
  • Leverages variety-seeking behavior or changing tastes

In email automation and drip campaigns, we have learned that with more personalization comes better response – and a greater likelihood of consumers responding to offers. But everything now comes through as personalized (except for the occasional misfire, like “Dear {FirstName}”), which slowly erodes that competitive edge. But all of this plays in the transactional space, devoid of motivation or emotion.

In our client work on customer value, we have noticed that measures related to the customer’s perception of his/her worth to the company is a far better predictor of customer retention than “shallow” measures of satisfaction or willingness to recommend. One measure (NPS), in particular, is especially weak in this area. Correlations with purchasing are always the lowest. This really should come as no surprise, since it is a thin ‘report card’. Management teams gain some comfort by following the herd who also uses NPS, but it provides little actionable insight into the underlying value equation.

We urge CMO’s and marketing leaders to think about longer-term results and to focus on the customer’s perception of their relationship, rather than the transactional value extraction approach embraced by many marketing organizations today.

As Peter Drucker famously noted, the purpose of business is not to make a profit; it is to create a customer.

How Survey Research Can Drive ROAS

How Survey Research Can Drive ROAS

Marketing research – notably strategic survey research – has not always provided useful insights for informing a company’s digital strategy. This is especially true for media targeting and ad spending. Why is this?
 
Well, simply put, insights – even from large-scale survey research studies (e.g., segmentation, or brand equity/perception research) – are typically conducted in a silo, and researchers tend to forget about the notion of linking survey sample sources (i.e., opt-in panels) to available external variables that could be used to make smart media planning decisions. And, because there are relatively few well-known use cases that can act as example, survey research is overlooked as a novel way to build out a digital marketing or media plan.
The utility of survey research would be vastly improved if it could provide the granular linkage needed for more precise targeting. Fortunately, this is changing fast.
Many survey research sample sources are now onboarded with pre-existing segmentation codes from 3rd party providers. This opens up entirely new ways to leverage survey research results – and directly shape targeting and media buying decisions in digital.
 
Demographic/psychographic coding is nothing new, and the results of pioneers like Claritas’ PRIZM were mixed. They were marginally useful in categories that had clear geo-demographic skews, such as those linked to income or ZIP code (e.g., autos, high-end appliances, etc.).
 
Be aware that external 3rd-party segmentation codes alone add almost no marginal value. In the case of a well-done segmentation study, distinct segments will likely emerge and 3rd-party codes would be redundant with what can be gleaned from the study itself. But… hang in there with me for another minute.
What does add significant value is how we go about piping the insights from the survey research side to the structured coding on the other side where media decisions must be made.
External 3rd-party codes connect us to the digital world. Once the linkage is built, digital targeting and ad spend opportunities become more clear. For example, Neustar’s E1 segmentation (172 segments, gasp) gives survey research new ways to add value in the digital world. Survey respondents appended with segment codes can be profiled on key survey questions (and vice versa). For example, do we really want to target those highly promotion sensitive people in segments 19, 72, and 113? This helps researchers identify optimal targets for digital and levels of spend. Our survey guidance helps both in targeting (e.g., LiveRamp) and allocating digital inventory (e.g., Trade Desk).
And media choices needn’t be entirely digital – they simply need to be addressable.
Media choices also include addressable TV and can address context. Another coding scheme from RMT assigns motivational profiles to both addressable TV content and individual respondents. Survey research can then combine respondent-level data with motivational profiles, and use that to identify TV shows that are in alignment with their belief systems and personal motivational outlook. Pretty cool.
 
Importantly, survey research is really the only tool that can effectively assess the issue of creative, which most experts say accounts for up to 80% of the impact of media spend.
 
For me, personally, survey research can clearly move up in prominence into a more useful advisory role. Survey research becomes a very useful tool in the marketer’s toolkit to drive brand growth and improve efficiency/performance/ROAS. One of the thought leaders in our industry, Joel Rubinson, has more to say here.
 
CPG/FMCG has had these tools available for a while now – notably retail scanner data, frequent shopper data, first party data, and customized purchase panels. They provide a steady data laboratory for CPG/FMCG experimentation and insights.
 
But I think the more interesting and exciting opportunity is in non-CPG/FMCG, especially for categories that do not have retail scanning, or 3rd-party verified POS/sales reporting.
Survey-based approaches can absolutely be used to shape digital messaging, targeting, and media planning/buying for non-CPG if done correctly.
So, whew, crazy right? Here are some thoughts/consideration factors as you begin to explore the possibility of utilizing survey research for digital targeting and media spending.
  • If you have only first-party data, onboard external segment codes if you can. This is the most immediate way to ease into the use of targeting methods, and you can begin to experiment and test. Don’t waste your time on A/B testing, which is great for refinement. Focus instead on targeting – that’s where the gold is!
  • Understand demographic and lifestyle identifiers (e.g., Neustar’s E1 as an example) that may have already been on-boarded by your retail scanning data provider or survey research sample providers.

  • In non-CPG/FMCG, look for opt-in sample source providers with segments already on-boarded, such as Dynata or Numerator.

  • If you have a very small brand, it makes sense to find those few segments allied with your brand. Your small brand may have a large share in key segments: identify segments with higher shares and target them. This can generate vast improvements in ROAS.

  • But don’t over-segment! Some segmentation approaches are absurdly huge, such as the popular Neustar E1 scheme (172 segments!). If you can, condense and work with a more modest approach, perhaps 20 or 30, as a first step.

  • For non-CPG/FMCG, work with a reputable researcher and conduct well-designed research with very large sample sizes to get adequate segment representation (remember, the goal is to link backwards).

  • Utilize demonstrably proven survey questions, such as constant sum, replacement vs. addition, share by use occasion, and so on to assess volumetrics. Note that we approach non-CPG/FMCG in the same way as CPG but replace scanning with survey data.

  • Take your high return segments and model them onto the larger universe of individuals aka “look alikes”.

  • Utilize the improvement in ROAS to reinvest in other “look-alike” segments, or in other geographies.
 
These are new and exciting opportunities! I believe that they will lead to a resurgence of survey research as a new method for optimizing digital targeting and media planning.
Get in touch to discuss further.
Attribution, Walled Gardens, and the Future of MTA

Attribution, Walled Gardens, and the Future of MTA

Marketers in the programmatic digital ad space are no doubt familiar with the term “multi-touch attribution”, or MTA. When we speak of attribution in this context, we are speaking of a specific digital “touch” in the customer journey to which we can attribute more or less weight that leads to a “conversion”. A conversion is something measurable: it can be the click of a mouse, a visit to a website, a sale, or some other behavior desired by the marketer. The availability of data at the granular level (individual, behavior, time) has led to the field of analysis known as MTA modeling. Each touch along the attribution train may carry more or less weight depending on where it falls in the sequence of touches for each individual consumer.
 
Now enter the more recent notion of “walled gardens” (aka the ‘attribution apocalypse’). Major providers of digital data, e.g., Facebook, Google, Amazon, Apple, smart TV manufacturers such as Samsung, digital ad exchanges, first-party providers, and any other sources that monitor digital journeys (aka ‘digital exhaust’), are starting to say “we don’t want to play with you”. They are beginning to erect significant barriers to digital data access due to two key factors:
 
  • Regulatory pressure about privacy (e.g., GDPR, California’s CCPA, and others), and;
  • Enlightened self-interest: digital data owners (e.g., Facebook) are preventing outside access, and by implication now claim that they are the best choice for performing any analysis of their digital warehouse (a little like grading your own homework). This entirely freezes out independent companies from performing cross-platform/device analysis.

 

 
The sheer availability of addressable digital data identifiers (e.g., cookies) is also changing. Google is joining Safari and Firefox in blocking third-party cookies in its Chrome web browser (phased out over the next two years). They are happy to go slow: Google makes its money on search and the ability to target. So Alphabet and Chrome are a bit at odds with each other. While cookies were never intended to share as much information as they currently do, how we will replace them will be fascinating. One solution, written about here before, is blockchain. This is an emerging technology that depends upon decentralized identities (either a public blockchain, or a private/consortium-style blockchain) with data that can be acquired or shared by media measurement companies and attribution consultancies.
 
Ultimately, this is an economic decision that has to be made by the end user. How much information is a browser user willing to share in exchange for the convenience and power of the tools they now use for free? And how much of that information are browser developers willing to share with the media measurement companies that want their data?
 
Several important constructs in how advertising actually works are overlooked in the rush to leverage the massive volumes of digital data used in MTA modeling. Data scientists lack industry knowledge about building awareness, memory and message decay, decreasing marginal returns in advertising, and other dynamics that involve brand choice/evoked set, or for that matter, emotion. Three simple examples: context (i.e., environment in which an advertisement is delivered); creative (i.e., the ability of advertising to break through and persuade); and brand (i.e., salience and momentum) have been, more or less, neglected. I have written about the power of great creative in sales forecasting. This concept applies to MTA as well.
 
The objective of all delivered media/advertising, and especially for MTA, is to “get the right ad to the right consumer at the right time”. The hidden assumption is that the consumer is always in the mood to receive the message, and that the consumer fully understands the message. In a world of screen clutter and six second ads, some companies are beginning to change their media and messaging strategy.
 
For example, P&G has shifted its focus to brand penetration (i.e., reach). Excess frequency (which MTA delivers well) has been criticized as wasting media dollars (as I noted, data scientists simply aren’t familiar with decreasing marginal returns in advertising spend). With the savings, P&G is (re)investing in reach. P&G’s Chief Brand Officer, Marc Pritchard, stated “The best measurement is people who are searching. So when we see an increase in search, we see an increase in sales.” This is largely consistent with the overall message of the book “How Brands Grow” by Bryron Sharp, which emphasizes this point and provides many data-supported case studies. Arguably, targeting is perhaps less critical if a company’s products have few demographic or media consumption skews. For others, precision targeting is essential.
 
Up until this point, media and marketing measurement firms have enjoyed a good ride with MTA (such a pun). First-mover companies who are nimble, smart, and have deep pockets can implement big data projects like MTA. And they are achieving significant ROAS – often in the very high double-digit range. And, these companies are able to adjust their models in real time and can continue to reap significant rewards. But, as more competitors build MTA models (or the technology becomes less costly, or the tasks less daunting), a company’s relative advantage will diminish. Think of it as an “MTA trickle-down effect”.
 
I wonder, in 10 years, whether MTA will be thought of as simply a targeting strategy to deliver excessive frequency for the short-term. Or, alternatively, a tool that really helped to build lasting brands and businesses. No doubt, the models will “learn” and become more precise and more “brand conscious”. One of the thought leaders in this space, Joel Robinson, often talks about “brand” vs. “performance” marketing. This is a very useful and provocative discussion: MTA penetration is now at 45% of US marketers based on the 2019 Mobile Marketing Association marketer study.
 
Until then, I hope we do not lose sight of what brands are all about, and that some aspects of brands are simply not measurable. That is, after all, the essence of brands – and marketing.
Surveys & Forecasts, LLC