Brandalyzer

Equity Theory of Motivation

Expectancy Theory of Motivation

This is a Harvard Business Review (HBR) post written by Ric Merrifield. Here is the link to the original article.

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STEVEN MOORE

Just as it’s hard to remember what life was like before the iPhone, it can be hard to remember business before there was CRM software — back when you still had to explain that it stood for “customer relationship management.” Today, CRM pervades the way many companies track and measure how they interact with other organizations, across many departments: marketing, sales, customer service, support, and others. CRM made it possible to determine precisely who responded to a specific marketing campaign and then who became a paying customer, which customer called the most for support, and so on. It gave companies some overall measure of revenue compared with marketing spend — something described in this 2007 article in The New York Times.

But now that Big Data and the Internet of Things have come along, we can go beyond the transaction to every little detail of the customer’s actual experience. You can know when customers enter your store, how long they are there, what products they look at, and for how long. When they buy something, you can know how long that item had been on the shelf and whether that shelf is in an area of things that usually sell fast or slowly. And then you can view that data by shoppers’ age, gender, average spend, brand loyalty, and so on. Today, this sort of thing is possible not just for online experiences; it’s possible for physical experiences as well — and not just retail shopping. This vivid view of the end-to-end experiences is rapidly changing the way people think about, measure, and manage their customer relationships.

Consider Waze, a wildly popular crowdsourced driving application that people use for real-time traffic information, warnings about hidden police, and turn-by-turn guidance about how to get around congestion. It only works because users give up their location information (on their mobile device) in exchange for information that will enhance their own experiences. So each mobile device pushes real time information into the main information hub, which processes all of the data and then pushes personalized messages back to every machine that is connected. It works incredibly well.

Another offering that taps the power of the Internet of Things and Big Data (and in whose development I was directly involved) is Disney’s MyMagic+. Disney customers (aka “guests”) wear a MagicBand bracelet that allows Disney to know where they are at all times. Guests use an application called My Disney Experience to plan all of their bookable and non-bookable activities: dining, rides, attending parades, and so on. Then, Disney can use the tracking information and send them personalized messages via their smartphones about things like where they might find a cold drink if they are ahead of schedule, what they might skip if they are running behind, and, if they are heading toward a congested area, a better route to take.

In addition, guests can use the band to get into their hotel rooms in their resort as well as the park (by tapping a Mickey Mouse icon instead of going through a turnstile). This has greatly increased the rate at which guests can enter the park.

Customers can also tap the MagicBand to pay for things, and scanners read it during rides. This means that after you finish a roller-coaster ride, you no longer need to go and find your picture and write down some 10-digit number if you want to buy a copy; all of those images show up on your My Disney Experience page so you can buy them at any time.

With every passing day there are more examples of Internet of Things adoption. And with every passing adoption, what people will accept (giving up things like their location information) and what they expect (“If Waze can help me with my driving, why can’t my grocery store tell me the fastest way to get through my grocery list?”) both change. As this intersection of what people will accept and what they expect evolves, the kinds of experiences that can be captured in the form of new big data evolves with it.

Now you can have visibility into everything. Not only can you tell that Customer A (who has a shopping app) went into a Lord & Taylor store to buy an expensive pair of shoes (which you could know with CRM). In addition, you can know how long they were in the store, where they walked, and whether they lingered or went straight to the shoe department and bought the shoes. Then, you can compare that visit to every visit to that store that Customer A has had (since getting the app), and you can at least infer what is most valuable to her. If she is always a get-in, get-out kind of shopper, speed of service may be her #1 thing. If she spends a great deal of time shopping, maybe price or product selection is her thing. If she buys a lot at the store, maybe she wants some form of recognition for her loyalty (whether it’s points or just a “welcome back”). If you compare online experiences with in-store experiences and weekend vs. weekday behaviors, your picture of the customer becomes three-dimensional very fast.

As exciting as it can be to talk about this and to see that it is happening right now in broad daylight, talking about how to assess customer experiences and how to engage customers differently when they have this information gets complicated quickly. The important thing is to acknowledge that the measurements of yesterday may need an overhaul, and to understand where your customers are on the acceptance-expectation path so you can try to stay with, if not get ahead of, them.  An increasingly common method for getting a handle on this is documenting the customer (and employee) experience journeys. What that means is examining high level areas such as:

  • Discover. How do they discover that they could have this experience?
  • Plan/Enroll. Do they need to sign up or enroll to have the experience?
  • Arrive. When do they begin to have the experience?
  • Engage. What are they doing while they are having the experience? What is their top priority (e.g., speed of service, best price, product selection)?
  • Complete. How do they choose to end their experience (e.g., when they are ready to leave that Lord & Taylor store)?  Not all experiences start and end with entering a physical location, but that is a common, simple, and familiar experience boundary.
  • Reflect. After the experience, do they visit social-media sites to comment on their experience, do they look at a loyalty-program site to ensure they got the points or credits that they expected?

Once the experience steps have been documented for the experience that occurred today, be specific about how you will make it different in the future because of the additional information that you have. Where will you and won’t you use that additional information? Will you greet them by name when they enter your store? Will you offer them what they had last time? Will you instruct your staff to leave them alone so they can feel anonymous? Many things are possible with all of this new experience information. Using it in a way that is aligned with your brand, improves the customer experience, and makes things easier for your staff are the things to prioritize and try.

Your plan probably won’t be perfect, and you may need to make some new hires, or get some outside help from people who have done these Big Data and Internet of Things projects. But whatever you do, don’t underestimate the impact of acting on something seemingly minute. The right incremental changes can truly transform the way people experience your organization. So even if something seems trivial, try it and see how it goes. You might find that you had underestimated just how powerful a small change can be.

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