Use People-Oriented Marketing: Because Products Change, But People Rarely Do

In 1:1 marketing, product-level targeting is “almost” taken for granted. I say almost, because most so-called personalized messages are product-based, rarely people-oriented marketing. Even from mighty Amazon, we see rudimentary product recommendations as soon as we buy something. As in: “Oh, you just bought a yoga mat! We will send you absolutely everything that is related to yoga on a weekly basis until you opt out of email promotions completely. Because we won’t quit first.”

In 1:1 marketing, product-level targeting is “almost” taken for granted. I say almost, because most so-called personalized messages are product-based, rarely people-oriented marketing. Even from mighty Amazon, we see rudimentary product recommendations as soon as we buy something. As in: “Oh, you just bought a yoga mat! We will send you absolutely everything that is related to yoga on a weekly basis until you opt out of email promotions completely. Because we won’t quit first.”

How nice of them. Taking care of my needs so thoroughly.

Annoying as they may be, both marketers and consumers tolerate such practices. For marketers, the money talks. Even rudimentary product recommendations — all in the name of personalization — work much better than no targeting at all. Ain’t the bar really low here, in the age of abundant data and technologies? Yes, such a product recommendation is a hit-or-miss, but who cares? Those “hits” will still generate revenue.

For consumers, aren’t we all well-trained to ignore annoying commercials when we want to? And who knows? I may end up buying a decent set of yoga mat cleaners with a touch of lavender scent because of such emails. Though we all know purchase of that item will start a whole new series of product offerings.

Now, marketers may want to call this type of collaborative filtering an active form of personalization, but it isn’t. It is still a very reactive form of marketing, at the tail end of another purchase. It may not be as passive as waiting for someone to type in keywords, but product recommendations are mixture of reactive and active (because you may send out a series of emails) forms of marketing.

And I’m not devaluing such endeavors, either. After all, it works, and it generates revenue. All I am saying is that marketers should recognize that a reactive product recommendation is only a part of personalization efforts.

As I have been writing for five years now, 1:1 marketing is about effectively deciding:

  1. whom to contact, and
  2. what to offer.

Part One is good old targeting for outbound efforts, and there are a wide variety of techniques for it, starting with rules that marketers made up, basic segmentation, and all of the way to sophisticated modeling.

The second part is a little tricky; not because we don’t know how to list relevant products based on past purchases, but because it is not easy to support multiple versions of creatives when there is no immediate shopping basket to copy (like cases for recent purchases or abandoned carts).

In between unlimited product choices and relevant offers, we must walk the fine lines among:

  1. dynamic display technology,
  2. content and creative library,
  3. data (hopefully clean and refined), and
  4. analytics in forms of segments, models or personas (refer to “Key Elements of Complete Personalization”).

If specific product categories are not available (i.e., a real indicator that a buyer is interested in certain items), we must get the category correct at the minimum, using modeling techniques. I call it personas, and some may call it architypes. (But they are NOT segments. Refer to “Segments vs. Personas”).

Using the personas, it is not too difficult to map proper products to potential buyers. In fact, marketers are free to use their imaginations when they do such mapping. Plus, while inferred, these model scores are never missing, unlike those hard-to-get “real” data. No need to worry about targeting only a small part of potential buyers.

What should a marketer offer to fashionistas? To trendsetters? To bargain seekers? To active, on-the-go types? To seasonal buyers? To big spenders? Even for a niche brand, we can create 10 to 20 personas that represent key product categories and behavioral types, and the deployment of personalized messages become much simpler.

And it gets better. Imagine a situation where you have to launch a new product or a product line. It gets tricky for the fashion industry, and even trickier for tech companies that are bold enough to launch something that didn’t exist before, such as a new line of really expensive smartphones. Who among the fans of cutting-edge technologies would actually shell out over a grand for a “phone”? This kind of question applies not just to manufacturers, but every merchant who sells peripherals for such phones.

Let’s imagine that a marketer would go with an old marketing plan for “similar” products that were introduced in the past. They could be similar in terms of “newness” and some basic features, but what if they differ in terms of specific functionality, look-and-feel, price point and even the way users would use them? Trying to copy some old targeting methods may lead to big misses, as even consumers hear about them from time to time.

Such mishaps happen because marketers see consumers as simple extensions of products. Pulling out old tricks may work in some cases, but even if just a small bit of product attributes are different, it won’t work.

Luckily for geeks like us, an individual’s behavior does not change so fast. Sure, we all age a bit every year; but in comparison to products in the market, humans do not transform so suddenly. Simply, early adapters will remain early adapters, and bargain seekers will continue to be bargain seekers. Spending level on certain product categories won’t change drastically, either.

Our interests and hobbies do change; but again, not so fast. It took me about two to three years to turn from an avid golfer to a non-golfer. And all golf retailers caught up with my inactivity and stopped sending golf offers.

So, if marketers set up personas that “they” need to push their products, and update them periodically (say once a year), they can gain tremendous momentum in reaching out to customers and prospects more proactively. If they just rely on specific product purchases to trigger a series of product recommendations, outreach programs will remain at the level of general promotions.

Further, even inbound visits can be personalized better (granted that you identified the visitor) using the personas and set of rules in terms of what product goes well with what persona.

The reason why models work well — man-made or machine-built — is because human behavior is predictable with reasonable consistency. We are all extensions of our past behaviors to a greater degree than the evolution rate of products and technologies.

Years ago, we’ve had a heated internal discussion about whether we should create a new series of product categories from VHS to DVD. I argued that such new formats would not change human behavior that much. In fact, genres matter more than video format for the prediction of future purchases. “Godfather” fans will buy the movie again on DVD, and then again in Blu-ray. Now some type of ultra-high-definition download from some cloud somewhere. Through all of this, movie collectors remain movie collectors for their favorite types of movies. In other words, products changed, but not human attributes.

That was what I argued then, and I still stand by it. So, all the analytical efforts must be geared toward humans, not products. In coming days, that may be the shortest path to fake human friendliness using AI and machine-made models.

 

Building Brand Trust Through Trusted Advocates

Nothing builds trust like a third-party endorsement; especially an endorsement from someone the consumer knows and trusts. Brand advocates extend your brand to their personal networks, generating more inherent trust among prospects. Customer advocacy and brand advocacy programs are interchangeable terms describing when companies cultivate brand advocates in a dedicated effort.

customeradvocacyNothing builds trust like a third-party endorsement — especially an endorsement from someone the consumer knows and trusts. Brand advocates extend your brand to their personal networks, generating more inherent trust among prospects. Customer advocacy, or brand advocacy, occurs when companies cultivate brand advocates in a dedicated effort.

A customer advocacy program aims to build consumer trust by increasing the volume of trusted voices on behalf of the brand. Brand advocates are most likely to be your customers or employees, but they could also be analysts, partners, writers or others involved with your industry, category, company, or products and services.

While advocates can appear naturally and organically, a successful customer advocacy program requires the structure, funding, time and talent to find, recruit and nurture these valued relationships. The program must meet the needs of both new and long-time advocates, from various locations, across target populations, in different channels, with different motivations and different response triggers.

It may seem like a monumental amount of work, but it will be worth it. All evidence suggests that quality personal recommendations and objective reviews highly impact buying decisions. And the results are even more exaggerated in decisions regarding technology, high-ticket items and B-to-B.

As consumers become less reachable through traditional advertising methods, a customer advocacy strategy becomes a necessity. The crux of a consumer advocacy program is finding the right advocates to engage in strategic brand conversations. These advocates may have a lot of followers and influence, or they may serve a niche audience. Most importantly, you want them to have passion and knowledge of your subject area and relevant topics to assure credibility. These advocates are often found on social media, but can also be gleaned from customer email lists and other channels.

Dedicate social listening and other research efforts to look for those with digital influence, quality content and brand affinity. You want them to already have a platform that you can enhance with product trials or betas, special access to company leadership, partnership opportunities and special offers for their followers. But reward their brand participation only through a completely transparent relationship, so as to protect the your public integrity and trust.

A brand with a customer advocacy mindset thinks of their advocates as more than opportunistic sources of content, leads or sales. Smart brands cultivate customer advocates as precious resources that create credibility and positive sentiment, reaching into and influencing populations the brand can’t touch as effectively itself. If a brand is authentic and responsive to these advocates, the relationship can start dialogue that returns immediate value.

The brand derives value from customer advocacy in numerous ways, including:

  • Frank feedback from knowledgeable and objective resources.
  • Reviews and testimonials that ring honestly to broad audiences.
  • Increased referral rates.
  • Humanization of the organization or brand.
  • An empowered staff.
  • Personalization of the customer experience.
  • Development of third-party resources, knowledge bases and assets.
  • Increased positive brand sentiment.
  • Increased overall awareness, share-of-voice and influence in your industry.
  • Increased leads and sales.

Tracking the value of an advocacy program requires the same strategic approach as other marketing program analytics. Start by crafting a goal statement that outlines specific, quantifiable objectives and then benchmark the appropriate KPIs. Regularly track and report against goals to keep the program performance on target, and to understand the relative value of different advocates. Look for impacts on business outcomes, not just measures of activity, to draw a straight line between this critical effort and your strategic business goals.

It is likely that your program analytics will identify some assets and channels that have more activity than others. Share these great stories and numbers with your team to develop key insights about your audiences and inform content planning across the organization.

Many organizations are investing in some of the activities that define a customer advocacy program but have yet to combine the elements into a cohesive plan under dedicated leadership with appropriate goals and funding. Plant the seeds for a true customer advocacy program by following these few key rules for advocacy within your organization:

  1. Earn Trust: Brand trust is essential to advocacy success. Organizations or brands challenged by scandal or disappointed customers should reform their business practices before attempting to encourage word-of-mouth marketing.
  2. Show Empathy: Understanding and communicating an emotional brand message will resonate with audiences in a way that other messaging approaches cannot.
  3. Focus on Quality: You don’t need the biggest network of advocates — you need the most impactful.
  4. Think Long Term: You will need to dedicate resources and incorporate advocacy activity into strategic planning.

Want to know more about building an effective customer advocacy program? View our free, one-hour webinar on the topic with audience Q&A, available here until 3/2/2017.

Election Polls and the Price of Being Wrong 

The thing about predictive analytics is that the quality of a prediction is eventually exposed — clearly cut as right or wrong. There are casually incorrect outcomes, like a weather report failing to accurately declare at what time the rain will start, and then there are total shockers, like the outcome of the 2016 presidential election.

screen-shot-2016-11-17-at-1-03-34-pmThe thing about predictive analytics is that the quality of a prediction is eventually exposed — clearly cut as right or wrong. There are casually incorrect outcomes, like a weather report failing to accurately declare the time it will start raining, and then there are total shockers, like the outcome of the 2016 presidential election.

In my opinion, the biggest losers in this election cycle are pollsters, analysts, statisticians and, most of all, so-called pundits.

I am saying this from a concerned analyst’s point of view. We are talking about colossal and utter failure of prediction on every level here. Except for one or two publications, practically every source missed the mark by more than a mile — not just a couple points off here and there. Even the ones who achieved “guru” status by predicting the 2012 election outcome perfectly called for the wrong winner this time, boldly posting a confidence level of more than 70 percent just a few days before the election.

What Went Wrong? 

The losing party, pollsters and analysts must be in the middle of some deep soul-searching now. In all fairness, let’s keep in mind that no prediction can overcome serious sampling errors and data collection problems. Especially when we deal with sparsely populated areas, where the winner was decisively determined in the end, we must be really careful with the raw numbers of respondents, as errors easily get magnified by incomplete data.

Some of us saw that type of over- or under-projection when the Census Bureau cut the sampling size for budgetary reasons during the last survey cycle. For example, in a sparsely populated area, a few migrants from Asia may affect simple projections like “percent Asians” rather drastically. In large cities, conversely, the size of such errors are generally within more manageable ranges, thanks to large sample sizes.

Then there are human inconsistency elements that many pundits are talking about. Basically everyone got so sick of all of these survey calls about the election, many started to ignore them completely. I think pollsters must learn that at times, less is more. I don’t even live in a swing state, and I started to hang up on unknown callers long before Election Day. Can you imagine what the folks in swing states must have gone through?

Many are also claiming that respondents were not honest about how they were going to vote. But if that were the case, there are other techniques that surveyors and analysts could have used to project the answer based on “indirect” questions. Instead of simply asking “Whom are you voting for?”, how about asking what their major concerns were? Combined with modeling techniques, a few innocuous probing questions regarding specific issues — such as environment, gun control, immigration, foreign policy, entitlement programs, etc. — could have led us to much more accurate predictions, reducing the shock factor.

In the middle of all this, I’ve read that artificial intelligence without any human intervention predicted the election outcome correctly, by using abundant data coming out of social media. That means machines are already outperforming human analysts. It helps that machines have no opinions or feelings about the outcome one way or another.

Dystopian Future?

Maybe machine learning will start replacing human analysts and other decision-making professions sooner than expected. That means a disenfranchised population will grow even further, dipping into highly educated demographics. The future, regardless of politics, doesn’t look all that bright for the human collective, if that trend continues.

In the predictive business, there is a price to pay for being wrong. Maybe that is why in some countries, there are complete bans on posting poll numbers and result projections days — sometimes weeks — before the election. Sometimes observation and prediction change behaviors of human subjects, as anthropologists have been documenting for years.