Measure Customer Experience Design And Make It Accountable

This article was originally published on Buyerlegends.com on April 29, 2015.

Buyer Legends are measurable and accountable by design. That is one of the important elements that distinguish Buyer Legends from any other business-storytelling and customer experience methodologies. A Buyer Legend is not a feel good story; it’s about business, and if your story doesn’t improve on your business goals, then what is the point?

Your Buyer Legend should describe in significant detail what actions you expect your customer to take, many of which are measurable. Pages viewed, transactions, subscriptions, store visits, phone calls, conversions to lead, and even social media engagement are all measurable.

Not All Customer Actions Are Created Equal

But they can all be useful to your optimization. In 2011, Bryan Eisenberg wrote:

If you are in retail, you want them to purchase a product.

If you are in lead generation, you want them to become a lead.

Are there no other actions that are valuable and contribute to the bottom line?

In retail, even if they don’t convert now, would it at least be more valuable to know if they added an item to their wish list, or subscribed to your newsletter, or looked up your retail store hours, or added items to their cart versus just bouncing off the site right away? What are you doing to turn that one-time customer into a repeat customer? Do they only need one product you sell or might they need different ones over the course of time?

In lead generation, if they don’t give you all their information and request to be contacted by sales, is it valuable to have them sign up for a whitepaper, or a demo, or your newsletter? Is it better to download specification sheets, engage in calculators, or print/forward pages rather than just bouncing off the website? These are all steps that move people through their buying process.

These are just some of your macro actions. What happens when someone comes from one of your ads and gets to a landing page? Sometimes the action is one of those listed above, but what if that page is only meant to help your visitors to choose the right product or service and they still need to actually click on the right one for them? What do you do to help them take that action and not bounce away? These are the micro actions that need to happen from step to step in the potential customer’s journey.

All of these are actions we need to optimize. You can calculate a conversion rate for each one of these macro and micro actions, and you should.

I wrote in a recent Buyer Legend Recipe Series post about persuasive momentum that whether or not you are aware, your business has created a de facto persuasive system. Buyer Legends is a process for creating a persuasive system that is intentional, measurable, and optimizable. That is why it is important for you to track both the micro and macro actions so that you are not just optimizing the final conversion, but all the steps in between where you can spot breakdowns in the system and fix them. Buyer Legends, done right, allow you to measure and optimize persuasive momentum.

While it is much easier to track and analyze online behavior, technology is making it possible to track and analyze in-store traffic as well as in-store behavior.

Mobile is growing so fast that many companies are seeing more traffic from tablets and smartphones than from desktops and laptops, and this trend is only growing. Google recently announced in that mobile near-me searches are up 3400% in the last three years.

A Legend for Your Customer’s Buying Journey

Your Buyer Legend is a map of your customer’s journey, and to read the map properly you need to have a legend. Here is the legend for your Buyer Legend from our book, Buyer Legends: The Executive Storyteller’s Guide:

Your hero is on a journey. You tell his or her story. Every successful customer journey needs a map and every map needs a legend. The journey’s legend is the key to navigating the map. See below the components of a legend.

Understanding the Value of Quantitative vs. Qualitative

We recently worked with a large data-driven technology company that had no shortage of quantitative data. In fact, they sent us gigabytes of it. We noticed that for every ten quantitative reports there was only one qualitative report. It was obvious to our team that their bias for hard data left them with a huge blind-spot. Quantitative data tell you WHAT your customers are doing, and qualitative data can provide insight into WHY your customers are doing what they do. They pointed out a problematic metric to us and asked us our opinion. A significant portion of new customers were using their software service once maybe twice and then falling out. We began a simple qualitative research exercise, we visited their sales call center and listened in on a several dozen calls. Soon the quantitative data began to make sense. We found that this company had such a strong brand that most people simply trusted the brand, so they signed up only to find that after using the software it wasn’t exactly the experience they expected. We couldn’t fix the software, so we solved the problem by helping them provide customers with the correct expectations in advance.

As human beings, our actions can be measured. This creates quantitative data. But the thoughts, emotions, and decision-making styles we use are subjective. They do have some degree of predictability, and this is qualitative. A business needs both types of research to see the whole picture. So, do not discount the value of focus groups, surveys, customer interviews, and even customer comments and reviews as you begin to craft your Buyer Legend.

Amazon is a great example of a company that uses both qualitative and quantitative. Never accused of being a warm and fuzzy guy, Jeff Bezos set Amazon on a course to be “the most customer-centric company on earth”. That involves not just knowing what customers are doing, but trying to understand why. Bryan Eisenberg wrote about Amazon’s Performance Secrets:

When Bezos decided to launch Amazon.com in 1994, he realized that the unique advantage of the Internet was the ability to programmatically learn more and more about your customer and personalize their experience. He realized that they could leverage every bit of data correlated with their customers’ personal unique identifiers (their email addresses) from each and every interaction. Amazon could learn from every sale, but also from every click, review, and mouse movement.

Three Examples of How To Measure Buyer Legends

Example #1 – an e-commerce Buyer Legend:

Marcy (hero) is frustrated that her microwave has broken (catalyst), so she moved it up on her to-do list to research and order a replacement today. She visits a handful of consumer sites, reads reviews, chooses the features she wants, lists a few possible models, and then measures the space in her kitchen to ensure that she doesn’t order a microwave that is too big or small. With measurements in hand, she is able to knock a handful of models off her list, leaving her with three choices. She goes to BestBuy.com, Sears.com, and Amazon.com to see more pictures, read more reviews, and compare prices. She notices that Best Buy has a price match guarantee but she will have to jump through too many hoops. Marcy is resourceful and frugal, and believes she can find the absolute lowest price for the microwave she wants. She does several Google searches, and visits a few sites but she is not impressed. The sites look unprofessional and the prices are all about the same.

Then, Marcy stumbles upon a website for Bob’s Appliance Outlet (measurable step). A large banner on the homepage announces to Marcy that most items qualify for free shipping (road sign), but even more impressive is a smaller banner in the top right corner of the page that says, “Want the lowest possible price? Make a price offer on any item in our store, and we will do our best to match it” (road sign). Marcy clicks on it (fork in the road), reads the next page and finds that the price offer feature is simple and straightforward with no fine print. She still wants to learn about a bit more about the company and goes to the About Us page (detour). After she reads this page she feels confident that this is a credible company with a credible offer. She then does a site search for the microwave she is looking for and finds it (measurable step). She reads through the product description and reviews for due diligence. She is pleased that her microwave qualifies for free shipping. Elated at the possibility of saving more than she expected, she enters an offer $100 dollars under the lowest price she found elsewhere and hits the Buy button (measurable step). A page comes back and tells her that her offer was too low but encourages her to try again. She didn’t really think they would accept another offer, but felt it was worth a try. She enters a price that is $50 under her previously lowest price, and this time the offer is accepted (destination). Marcy is presented with a page that congratulates her and tells her that her item will likely ship today and asks her how she would like to be notified about shipping. She chooses text message over email or automated phone call. Marcy goes to the kitchen satisfied, and pours herself a cup of tea, She crosses Find New Microwave off her to-do list, and begins the next item on the list.

Example #2 B2B lead generation Buyer Legend:

Mark (hero) is a savvy entrepreneur who is looking to expand by opening up a 4th location in the greater Phoenix area (catalyst). Mark used some pricey consultants in the past with mixed results. Someone told him about Idealspot.com so he went to the homepage (first measurable step), and when he saw the word algorithm, he immediately lost confidence. Mark simply believed that an automated computer process could not possibly find him a great location, so he leaves and forgets about Idealspot.com (detour).

A week later Mark is on LinkedIn and sees a ‘re-targeted’ ad with the headline, “How Science and Big Data Are Changing the Way Businesses Choose New Locations”. Not recognizing this as a post from the Idealspot.com blog, he is intrigued and clicks through (measurable step). He reads about how big data is able to spot success patterns. It explains that most location analyses hit the wall when people become involved in spending time and money collecting piles of data, but then have no way to relate it to the success or failure of their business. This is where big data and learning algorithms inject science into the process by mining through the data to pick out those patterns of success or failure and the key factors driving those patterns. The algorithms act without human bias; they start from scratch and come up with a model that is unique for each business based purely on results. Mark is starting to understand the value of Idealspot.com; he had assumed that human involvement was superior, but now he began to doubt that premise. Mark clicks through to the Idealspot.com How Does it Work page (measurable step).

Mark reads about the algorithm and how the data is loaded for each location, and how the success-prediction clientele are chosen, based on competitors and his type of business. He sees this is similar, even superior, to the methods used by much more expensive location-research alternatives. Mark starts to feel excited.

Mark wants to get a sense of the Idealspot.com track record, so he clicks on the Success Stories page (fork in the road) and reads a handful of stories by clients who are experiencing early success. He sees that Idealspot.com is a startup and their term track record is not as long or established as it could be, but the low introductory price of $297 removes this barrier from his mind.

Mark wants to try Idealspot.com. Still believing the pricing is too good to be true, Mark reads a section on the Pricing page (detour) that explains how big data and learning algorithms dramatically reduce the cost of research allowing IdealSpot to offer high-value analyses and rock bottom prices (road sign).

He clicks the Get Started button (measurable step). It explains the cost of each report, and that he is setting up an account that will allow him to enter potential locations and request as many or as few reports as needed. He does not need a credit card right now.

Marks appreciates that his privacy will be protected.

Mark fills out a form requesting his name, email and password, and then clicks Join and creates an Idealspot.com account (destination). He is excited to start scouting locations and using Idealspot.com for feedback.

Example #3 B2C multi-channel Buyer Legend:

When Debbie (hero) turned 12, her Aunt Rebecca bought her a charm bracelet with a collection of charms. Debbie loved it, and 29 years later she still wears it. And now her 11 year old daughter Ashley is coming up on a birthday. Ashley loves her mom’s charm bracelet, and is always looking through the charms and asking questions. She even asked to borrow it for a night out with a friend. Debbie of course wants to surprise her daughter on her birthday with an impressive bracelet and nice collection of charms to get started (catalyst).

While out and about running errands she takes a moment to search Google on her Android phone for “Charm Bracelets nearby”. Of course, she sees Pandora at the mall but thinks they are overpriced. She also finds a Charm Boutique and decides to drop by to see what they have. As she walks in (first measurable step) she is is greeting warmly and encouraged to take her time look around and then just ask if she needs help.

Debbie is impressed with the store; their oversized charms hang in the windows and from the ceiling. It is a fun atmosphere, where she can imagine returning with her daughter and buying new charms in the future. As Debbie scans the merchandise under the glass she sees several bracelets, none of which she think would match her daughter’s taste. She asks if they have any more styles and the saleswoman takes her to a computer and shows her several more designs that are available online or by special order (road sign). She zeroes in on a style and asks about it. The sales woman tells her that it is on back order and it may take several weeks to Special Order, but that it may be available online. Debbie asks her to please write the model and style number down for her and then turns her eyes to the charms. They have an impressive collection but she can’t find a couple of essential charms she would need. Ashley and she share a love of folk music and spend a few evenings a month playing guitar and singing, so a guitar charm is a must. Ashley also loves and collects zebras but the store has none of those, either. While there, she picks up a handful of charms that Ashley would love (measurable step) and heads home (detour).

That night after Ashley falls asleep Debbie goes online to visit the Charm Boutique website (measurable step) and quickly gets lost in the selection. She finds the bracelet she liked at the store as well as a guitar charm, a zebra charm, and about a dozen others that she adds to her cart, satisfied she has found the perfect Birthday gift for Ashley. She hits the checkout button and sees the total. It’s a little more than she wanted to spend. So Debbie visits the Pandora website to compare charms and pricing (detour). She finds that many of the charms she wants are there, but not all, and the bracelet choices are not that great. Even more so when she places them in her cart and hits checkout. The price is much more than that of Charm Boutique. So, she goes back to the Charm Boutique site, and finds something she missed before. She sees that her order qualifies for free priority mail shipping and she could have it in a week, giving her plenty of breathing room before Ashley’s birthday. She finishes checking out and is tickled that this worked out so well. She can’t wait to see the look on her daughter’s face when she opens this present.