How Being the Change You Want to See Can Also Increase Profits

Being the change needed in your world takes courage. It can be risky. But in the end, it pays off. Not just because you and your entire staff feel good about doing something good for others, but because you will also see the dividends.

In business, we are so accustomed to operating and marketing to reach the primary goals of maximizing ROI and profits. Daily, we analyze our financial statements, expenses, and operations costs to find where we can make more money. We review our pricing to see if we can increase our margins. And we analyze our marketing data to see which customers have more sales and profit potential. And then we continue to strategize how we can do more, get more and be more. It’s all part of the exciting game of business, and how we are wired to perform. And it’s what our careers are built upon.

But What If …

We operated from a different perspective? What if we built business models on the greater good instead of ROI and profitability? What if we priced to make our products and services more accessible to all in need instead of desired profit margins? While this may sound defeating and even a little silly, here’s something to think about.

Those of us with pets are constantly faced with exorbitant prices to provide the care our pets need. Like human healthcare, the prices are mind-boggling. And often, we simply cannot afford the prices that veterinarians charge, so our beloved pets go without surgeries and other care they need.

being the change photo
Credit: Getty Images by Hero Images

Recently, my beloved dog needed some tumors removed. Vets I called wanted around $2,000 for the procedure that would not take even an hour. Someone recommended a new vet in town to me, one who had built a business model around affordable surgical procedures. Her prices were about ¼ of what most vets charged, because she only did surgical and dental procedures, had a staff of one, no partners to pay dividends to, and did not have a big facility that added to big overhead costs. Her business model was simple, and so too her pricing.

Before taking my dearly beloved canines to her, I stopped to meet her to make sure she was real and had the certifications and licensed seal of approval in her office. I told her that her low pricing made me wonder and asked why she did this.

Her response:

“I am tired of seeing animals not get the care they need because the humans that love them can’t afford it.”

I was touched by this response. I took both my dogs to her and seriously had the best outcomes I’ve ever had. The quality of her work was best-in-class, and her pricing surreal — in fact, I felt guilty paying so little. I was so emotionally energized by her work and her commitment to doing what was right for more than just herself that I took to Facebook. I posted about Dr. Natalie from the The Eagle Pet Vet in Eagle, Colo., and the amazing quality of care my dogs got for a fraction of others’ prices. I thanked her publicly for her devotion to animals and to creating a business model based on the greater good vs. the great revenue stream.

Within hours, my post got a record high number of likes for this Community Page and dozens of comments from other patients with their similar stories and posts of appreciation and affection for Dr. Natalie and her mission, which made it possible for so many to afford needed care for dogs and cats.

The response to this post was not just inspiring to see how people truly appreciated Dr. Natalie for her business model, but to see how people respond to a business that operates to serve more than just its own interests. Dr. Natalie seriously charges ½ to ¼ what others do for the same procedures. And her business thrived because of the word of mouth of all those she served. In just two years of operation, she built not only a loyal clientele, but a large following — as people love talking about her and recommending her because of the joy she brought to their lives by enabling them to care for their pets the way they want to by making that care affordable.

How can businesses of all sizes across all industries learn from Dr. Natalie?

  • For one, like no one I’ve ever met before, Dr. Natalie built a business around being the change she wanted to see in the pet industry. The first step to building a business that creates the strong emotional bonds customers have for Dr. Natalie and her business is to face the change needed in your industry to benefit all involved. Businesses, customers, and communities. What is that change?
  • Second, how can you adapt your business around it? While you might not be changing your pricing model, or reducing your overhead anytime time soon, how can you build a special offering, payment plans, or scaled-down product around the change your industry needs in order to serve more people than just those able to use your services now?

Being the change needed in your world takes courage. It can be risky. But in the end, it pays off. Not just because you and your entire staff feel good about doing something good for others, but because you will also see the dividends.

We all know the best form or marketing is earned, not paid. And there is no price you can put on the kind of comments my Facebook post generated for this small pet clinic in a small Colorado town.

It’s hard to get people talking about your business — but when you see how easily people spread the good word about a business doing good, without being asked to post, refer, or recommend, it can seriously change the way you think about marketing and sales.

Here’s to the wonderful inspiration from Gandhi, who taught us all to be the change we wanted to see in the world:

“We but mirror the world. All the tendencies present in the outer world are to be found in the world of our body. If we could change ourselves, the tendencies in the world would also change. As a man changes his own nature, so does the attitude of the world change towards him. This is the divine mystery supreme. A wonderful thing it is and the source of our happiness. We need not wait to see what others do.”

How Marketers Can Throw Away Data, Without Regrets

Yes, data is an asset. But not if the data doesn’t generate any value. (There is no sentimental value to data, unless we are talking about building a museum of old data.) So here’s how to throw away data.

Last month, I talked about data hoarders (refer to “Don’t Be a Data Hoarder”). This time, let me share some ideas about how to throw away data.

I heard about people who specialize in cleaning other people’s closets and storage spaces. Looking at the result — turning a hoarder’s house into a presentable living quarters — I am certain that they have their own set of rules and methodologies in deciding what to throw out, what goes together, and how to organize items that are to be kept.

I recently had a relatable experience, as I sold a house and moved to a smaller place, all in the name of age-appropriate downsizing. We lived in the old home for 22 years, raising two children. We thought that our kids took much of their stuff when they moved out, but as you may have guessed already, no, we still had so much to sort through. After all, we are talking about accumulation of possessions by four individuals for 22 long years. Enough to invoke a philosophical question “Why do humans gather so much stuff during their short lifespans?” Maybe we all carry a bit of hoarder genes after all. Or we’re just too lazy to sort things through on a regular basis.

My rule was rather simple: If I haven’t touched an item for more than three years (two years for apparel), give it away or throw it out. One exception was for the things with high sentimental value; which, unfortunately, could lead into hoarding behavior all over again (as in “Oh, I can’t possibly throw out this ‘Best Daddy in the World’ mug, though it looks totally hideous.”). So, when I was in doubt, I chucked it.

But after all of this, I may have to move to an even smaller place to be able to claim a minimalist lifestyle. Or should I just hire a cleanup specialist? One thing is for sure though; the cleanup job should be done in phases.

Useless junk — i.e., things that generate no monetary or sentimental value — is a liability. Yes, data is an asset. But not if the data doesn’t generate any value. (There is no sentimental value to data, unless we are talking about building a museum of old data.)

So, how do we really clean the house? I’ve seen some harsh methods like “If the data is more than three years old, just dump it.” Unless the business model has gone through some drastic changes rendering the past data completely useless, I strongly recommend against such a crude tactic. If trend analysis or a churn prediction model is in the plan, you will definitely regret throwing away data just because they are old. Then again, as I wrote last month, no one should keep every piece of data since the beginning of time, either.

Like any other data-related activities, the cleanup job starts with goal-setting, too. How will you know what to keep, if you don’t even know what you are about to do? If you “do” know what is on the horizon, then follow your own plan. If you don’t, the No. 1 step would be a companywide Need-Analysis, as different types of data are required for different tasks.

The Process of Ridding Yourself of Data

First, ask the users and analysts:

  • What is in the marketing plan?
  • What type of predictions would be required for such marketing goals? Be as specific as possible:
    • Forecasting and Time-Series Analysis — You will need to keep some “old” data for sure for these.
    • Product Affinity Models for Cross-sell/Upsell — You must keep who bought what for how much, when, through what channel type of data.
    • Attribution Analysis and Response Models — This type of analytics requires past promotion and response history data for at least a few calendar years.
    • Product Development and Planning — You would need SKU-level transaction data, but not from the beginning of time.
    • Etc.
  • What do you have? Do the full inventory and categorize them by data types, as you may have much more than you thought. Some examples are:
    • PII (Personally Identifiable Data): Name, Address, Email, Phone Number, Various ID’s, etc. These are valuable connectors to other data sources such as Geo/Demographic Data.
    • Order/Transaction Data: Transaction Date, Amount, Payment Methods
    • Item/SKU-Level Data: Products, Price, Units
    • Promotion/Response History: Source, Channel, Offer, Creative, Drop/Wave, etc.
    • Life-to-Date/Past ‘X’ Months Summary Data: Not as good as detailed, event-level data, but summary data may be enough for trend analysis or forecasting.
    • Customer Status Flags: Active, Dormant, Delinquent, Canceled
    • Surveys/Product Registration: Attitudinal and Lifestyle Data
    • Customer Communication History Data: Call-center and web interaction data
    • Online Behavior: Open, Click-through, Page views, etc.
    • Social Media: Sentiment/Intentions
    • Etc.
  • What kind of data did you buy? Surprisingly large amounts of data are acquired from third-party data sources, and kept around indefinitely.
  • Where are they? On what platform, and how are they stored?
  • Who is assessing them? Through what channels and platform? Via what methods or software? Search for them, as you may uncover data users in unexpected places. You do not want to throw things out without asking them.
  • Who is updating them? Data that are not regularly updated are most likely to be junk.

Taking Stock

Now, I’m not suggesting actually “deleting” data on a source level in the age of cheap storage. All I am saying is that not all data points are equally important, and some data can be easily tucked away. In short, if data don’t fit your goals, don’t bring them out to the front.

Essentially, this is the first step of the data refinement process. The emergence of the Data Lake concept is rooted here. Big Data was too big, so users wanted to put more useful data in more easily accessible places. Now, the trouble with the Data Lake is that the lake water is still not drinkable, requiring further refinement. However, like I admitted that I may have to move again to clean my stuff out further, the cleaning process should be done in phases, and the Data Lake may as well be the first station.

In contrast, the Analytics Sandbox that I often discussed in this series would be more of a data haven for analysts, where every variable is cleaned, standardized, categorized, consolidated, and summarized for advanced analytics and targeting (refer to “Chicken or the Egg? Data or Analytics?” and “It’s All about Ranking”). Basically, it’s data on silver platters for professional analysts— humans or machines.

At the end of such data refinement processes, the end-users will see data in the form of “answers to questions.” As in, scores that describe targets in a concise manner, like “Likelihood of being an early adopter,” or “Likelihood of being a bargain-seeker.” To get to that stage, useful data must flow through the pipeline constantly and smoothly. But not all data are required to do that (refer to “Data Must Flow, But Not All of Them”).

For the folks who just want to cut to the chase, allow me to share a cheat sheet.

Disclaimer: You should really plan to do some serious need analysis to select and purge data from your value chain. Nonetheless, you may be able to kick-start a majority of customer-related analytics, if you start with this basic list.

Because different business models call for a different data menu, I divided the list by major industry types. If your industry is not listed here, use your imagination along with a need-analysis.

Cheat Sheet

Merchandizing: Most retailers would fall into this category. Basically, you would provide products and services upon payment.

  • Who: Customer ID / PII
  • What: Product SKU / Category
  • When: Purchase Date
  • How Much: Total Paid, Net Price, Discount/Coupon, Tax, Shipping, Return
  • Channel/Device: Store, Web, App, etc.
  • Payment Method

Subscription: This business model is coming back with full force, as a new generation of shoppers prefer subscription over ownership. It gets a little more complicated, as shipment/delivery and payment may follow different cycles.

  • Who: Subscriber ID/PII
  • Brand/Title/Property
  • Dates: First Subscription, Renewal, Payment, Delinquent, Cancelation, Reactivation, etc.
  • Paid Amounts by Pay Period
  • Number of Payments/Turns
  • Payment Method
  • Auto Payment Status
  • Subscription Status
  • Number of Renewals
  • Subscription Terms
  • Acquisition Channel/Device
  • Acquisition Source

Hospitality: Most hotels and travel services fall under this category. This is even more complicated than the subscription model, as booking and travel date, and gaps between them, all play important parts in the prediction and personalization.

  • Who: Guest ID / PII
  • Brand/Property
  • Region
  • Booking Site/Source
  • Transaction Channel/Device
  • Booking Date/Time/Day of Week
  • Travel(Arrival) Date/Time
  • Travel Duration
  • Transaction Amount: Total Paid, Net Price, Discount, Coupon, Fees, Taxes
  • Number of Rooms/Parties
  • Room Class/Price Band
  • Payment Method
  • Corporate Discount Code
  • Special Requests

Promotion Data: On top of these basic lists of behavioral data, you would need promotion history to get into the “what worked” part of analytics, leading to real response models.

  • Promotion Channel
  • Source of Data/List
  • Offer Type
  • Creative Details
  • Segment/Model (for selection/targeting)
  • Drop/Contact Date

Summing It All Up

I am certain that you have much more data, and would need more data categories than ones on this list. For one, promotion data would be much more complicated if you gathered all types of touch data from Google tags and your own mail and email promotion history from multiple vendors. Like I said, this is a cheat sheet, and at some point, you’d have to get deeper.

Plus, you will still have to agonize over how far back in time you would have to go back for a proper data inventory. That really depends on your business, as the data cycle for big ticket items like home furniture or automobiles is far longer than consumables and budget-price items.

When in doubt, start asking your analysts. If they are not sure — i.e., insisting that they must have “everything, all the time”— then call for outside help. Knowing what to keep, based on business objectives, is the first step of building an analytics roadmap, anyway.

No matter how overwhelming this cleanup job may seem, it is something that most organizations must go through — at some point. Otherwise, your own IT department may decide to throw away “old” data, unilaterally. That is more like a foreclosure situation, and you won’t even be able to finish necessary data summary work before some critical data are gone. So, plan for streamlining the data flow like you just sold a house and must move out by a certain date. Happy cleaning, and don’t forget to whistle while you work.

Disruption Drives the NFL to Gamble — Or How to Kill a Sacred Cow in 3 Easy Steps

A couple weeks ago, the NFL discussed gambling and the game. … No, not the impact gambling could have on the game. Actual gambling as part of the NFL — from your seat, at the game or in your home, every Sunday. Why would they do that? Disruption. What will you do when the sacred cows in your industry are brought to the butcher’s block?

NFL considers killing a sacred cow and allowing gambling back ino the game.
Credit: Pixabay by Keith Johnston

A couple weeks ago, the NFL had a summit to discuss gambling and the game. … Not the impact gambling could have on the game; actual gambling as part of the NFL — from your seat, at the game or in your home, every Sunday.

If they do that, it will mark the death of one of America’s most sacred cows: the separation of the big four team sports — football, baseball, hockey and basketball — from dirty, dirty gamblers who could taint the games. It could also bring Brink’s trucks full of even more money into the league coffers.

Why would they do that? Disruption.

Like many industries, the NFL sees a game-changing event on the horizon. The owners need to decide whether they want to stay the course (and potentially see someone else benefit from that disruption), or move first to make the most of it (and potentially ruin everything they’ve built).

If your industry hasn’t faced this kind of decision, it will. What will you do when your sacred cows are brought to the butcher’s block? Here are three steps to think through whether to keep Bessy on her pedestal, or make the hard cut.

1. Recognize the New Situation

This sacred calf has been venerated for nearly 100 years — ever since the “Black Sox” scandal, when the Chicago White Sox were accused of throwing the 1919 World Series. That was when the big leagues realized gambling could undermine the legitimacy of sports in the eyes of the American public (and any sports fan will tell you the referees do a good enough job of that all on their own).

While U.S. attitudes toward gambling have changed in the past decade, for most of my life, the idea of league-sanctioned gambling was absolutely unthinkable. Now, NFL ownership is considering not just whether they could cozy up with casinos, but how they might do it, and how many zeros those checks might have.

It could be the boldest stroke of genius, or the dumbest butt-fumble, in NFL history.

via GIPHY

Gambling exists in a grey area of American entertainment. By and large, sports gambling has been limited to just Las Vegas in the United States. Now the Supreme Court appears ready to allow New Jersey to add sports gambling to its casino and race track games, and that would open the floodgates for other states to do the same.

This is a remarkably new situation for the NFL. Gambling may be coming, and the owners would rather ride that wave than be drowned by it.

At the same time, some of the underlying realities of the Black Sox scandal have changed as well. Athletes of the time were not that wealthy, and very vulnerable to outside financial influence. Today, professional athletes are some of the wealthiest people in the world, and gambling payoffs large enough to motivate them seem unrealistic. What’s more, Europe’s soccer leagues have been in bed with gambling for years, and the nightmare scenarios just haven’t materialized (although it hasn’t been all clean, either).

All of those factors mean the context that made this cow sacred have changed. And the business people who’ve been holding it sacred need to recognize that, too.

2. Identify the Business Opportunities

It’s one thing to recognize the situation has changed. It’s another to identify the opportunities in it.

Machine Learning and AI — What’s ‘Real,’ What’s Required

Big data has gone full-cycle. Quite a while ago, big data had its beginning within the realm of academic research. Recognizing its usefulness, niche businesses then began implementing big data. Massive companies, such as Google, began commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big. This all makes for a lot of noise in the marketplace.

Data graphicBig data has gone full-cycle.

Quite a while ago, big data had its beginning within the realm of academic research. Niche businesses then began implementing big data after recognizing its usefulness. Next, massive companies (like Google) started commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big.

This all makes for a lot of noise in the marketplace.

Today, we hear folks without applied mathematics or computer science backgrounds talking big data, algorithms and artificial intelligence (AI) at cocktail parties. The fluency has grown rather quickly: A CMO I’ve known for years used to wince when we talked analytics, but now she enthusiastically discusses her firm’s AI initiatives. She’s not running marketing at Google or IBM Watson, either — she sells clothing online.

While we’re likely in one of the most amazing periods in history to be in business, it does not come without its challenges. These days, you have to sift through all of the clutter when it comes to innovations in the marketing space.

Let’s see if we can simplify what the data pundits are tweeting and discern where the value really is.

Machine Learning

Machine learning (ML) occurs through networks of algorithms.

First, the good news: ML really works.

As we’ve discussed in “Marketing Machines — Possible or Pipedream?” ML is used to ingest large amounts of data and identify patterns in that data. The machine “learns” by ingesting, transforming and then conditioning a learning algorithm with your dataset.

ML will find the statistical relationships (models) between your various data points to articulate how efficiently your business is running. By calculating the best potential models, it can also show you what improvements you can make. ML can deduce your most profitable business targets. It can tell you who is likely to buy shoes priced over $800, or which production line is most likely to break down in the wintertime.

But ML Isn’t Foolproof

Machine Learning can surely help us find structure and patterns in data through statistics and the power of cloud computing. Amazon’s ML cloud computing capability, for example, isn’t specific to any domain and arguably works with any inputs. It will consistently output a result or target. Yet that very flexibility is where ML can prove risky:

“If you can dump anything into an ML process, and have it come up with an answer, you’d be wise to be wary of that answer.”

ML techniques all require you provide it with a “universe”. This universe consists of all the likely permutations representative of your purpose. If your conditioning data is skewed heavily to sneakers under $75, it will prove very hard to predict what customers are likely to buy $800 shoes.

This may sound like an unfair example, but consider the marketers who are out to break into the higher-end sales but only have data from their pre-existing customers. If skewed interpretations were applied to new-customer marketing (and they can be), your returns could be even worse than without any ML interference. The fact is, there are far more experiments where ML doesn’t produce a valuable outcome than those that do. But as technology and big data are refined over time, better results will be achieved across the board.

Analytics and model-building are highly iterative processes. If an ML process is focused on only a particular niche, the likelihood of getting better results sooner is higher — but still iterative. Despite its current limits, AI offers a deeper and more layered method of applying iterative math to break down large data questions than raw manpower.

Google’s AlphaGo AI beat champion Lee Sedol in a tournament of Go by exponentiating component questions, covering as many bases as it could. While AlphaGo works similarly in many ways to the human mind in this way, it did also have the advantage of iteratively playing against itself thousands of times.

Humans can’t do that.

The Bottom Line: Good Data In, Good Comes Out

Whether Google’s AlphaGo, Amazon’s ML tools or your home-grown mashup, the quality of the data that goes into ML is the largest factor you can control in creating value with systems-driven optimization.

In an age where many organizations have siloed data or cumbersome messes, along with marketing organizations that don’t even have a reliable marketing operations database, this is no small challenge. Getting your data centralized, organized and accessible is a requisite first step. Get that right, and there may be opportunities ahead to drive value up.