There's nothing like telling the audience I am speaking at a conference or I am publishing a new booklet to drive increased unsubscribes. Given that I'm going to spend this entire week talking about the VT/NH event on March 30, you might not like the ...

 

Kevin Hillstrom: MineThatData - 5 new articles



I Can't Take Action ...

There's nothing like telling the audience I am speaking at a conference or I am publishing a new booklet to drive increased unsubscribes.

Given that I'm going to spend this entire week talking about the VT/NH event on March 30, you might not like the content ... if it really bothers you, please unsubscribe right now. It's ok, no worries.

Here's a link to the event (click here). Please join us! It costs you next to nothing and the VT/NH do a great job and you won't be swarmed by a 60/40 vendor mix when you walk down the hall. It's you and your peers.

Ok, why am I hosting a business simulation instead of giving a two hour talk about metrics or customer acquisition or merchandise productivity?

Well, Bill runs a conference where you don't have to pay $20,000 to sell a message to the audience and appreciates honest perspectives (even if everybody in the audience is not going to agree) and that is greatly appreciated. So that's a big part of it. It's terribly hard in our modern world to not get trapped by a conference that has "scale" ... you get seduced into bringing an NFL quarterback into the fold or an NBA legend or you host a nighttime event with a popular band from the 1990s ... and you get seduced into vendor nonsense. Yes, you have a large audience and you have money ... but what good is that if you are conference organizer? You have to help people perform better, don't you?

So that's one reason I am running a business simulation - to help you perform better at work.

Here's the other reason I am running a simulation instead of offering 188 slides about metrics or customer acquisition or merchandise productivity. The reason = Action + Accountability.

When you present a couple hundred slides to fifty or three-hundred or fifteen hundred people, you get a lot of feedback. In the past two years, here's the number one comment I received as feedback:
  • "This is great, but I can't take action because of ...."
"I can't take action."

When you can't take action, you look for metrics and/or facts that will convince somebody else to take action. Now, I could craft a 200 page presentation with the metrics that I recommend you use - but your situation is "local" and my metrics are "global" and so the presentation would end up a failure, because the presentation doesn't address the core issue. The core issue, of course, is "why" you cannot take action.

I've learned that there are three reasons people don't take action.
  1. Leadership wants to "control" things.
  2. People know what to do but are afraid of the consequences of being wrong.
  3. People don't know what to do.
So if I remove Leadership/Control from the equation ... and if I remove the consequence of being wrong ... and if people who don't know what to do get to see what people who know what to do actually do ... then I've removed "why" from the equation. And if the "why" has been removed from the equation, then we should be in a fun situation, a learning situation, where Action + Accountability is rewarded ... and if the participant doesn't perform well it's ok because the participant gets to see the actions that led to a positive outcome.

Hence - I'm presenting a five-year business simulation on March 30.

There will be 20 teams.

Each team will make their own decisions.

Each team will get to see the decisions made by other teams.

Everybody learns.

Nobody is put in a "bad situation".

What's not to like about this?

How about joining me on March 30? Click here for details. More on the topic tomorrow.



         
 
 

A Month Before The Simulation

Sure, you could spend a thousand dollars attending a big conference and be entertained by a Sports Legend ... but what would you actually learn if you did that? Is that the best use of company resources?

Or you could join me on March 30 (click here for details). You could compete against other attendees - 20 teams in total - trying your hardest to grow a business from scratch.

You will get to make the decisions.
  • How much should I invest in offline marketing?
  • How much should I invest in online marketing?
  • How many people should I hire of offline, online, and mobile/social activities?
  • How much should I charge for each of three product categories?
  • Should I employ free shipping, shipping with a hurdle, or ask the customer to pay for shipping?
Once you make the decisions, the simulation worksheet produces outcomes. You'll get to see how your team performed. You'll also get to see how the other teams performed. You will get to see the investment/staffing/pricing strategy for each team.

Then, you'll get an opportunity to make changes to your strategy for year two. So will everybody else.

We'll repeat the process for five years. At the end of five years, the team with the best combination of sales growth and profitability will "win" - and earn a coveted MineThatData Pick Axe!

What will you learn?
  • You will learn how having access to the same metrics as everybody else yields wildly different strategies.
  • You will learn that success comes from the strategies you employ as a result of the metrics you analyze - you will learn that metrics do not yield success - you create your own success.
  • You will learn that there are many different "winning strategies" built into the simulation ... you will learn how important your own decisions are.
What would stop you from joining me, from participating in a day-long extravaganza of learning?

         
 
 

Real Estate / Analytics / Finance Models and Store Closures

Most large retail brands have a Real Estate department. This department finds the best places to build new stores, and works with Finance and Strategy to determine markets to move in/out of.

In recent years, Real Estate departments have partnered with Analytics departments to understand the impact of online sales on existing / new store locations. The Finance folks appreciate this partnership, because it is becoming harder to justify keeping open B/C locations.

Let's review (at a very high level) the factors that come into play when determining if a store should be closed.


First, most large retailers maintain a "strategy worksheet" for each existing store and each potential new location. The strategy worksheet lists a series of metrics about each store (demographic metrics, performance metrics, competitive metrics, and channel-centric metrics). When evaluating new locations, each new location is compared against existing locations - finding "comps" where applicable.

Second, forecasts for each of the next five years are produced for existing stores. The forecasts take into account trends across the customer base (i.e. 40% of 12-month buyers will purchase again next year, spending $200 each, while the store will attract "x" new customers spending $130 each - yielding a forecast). The forecasts take into account online cannibalization (i.e. as the web grows, the web pushes customers into stores - and as the web grows, the stores push customers online at a faster rate). The forecasts account for competitive issues. If Macy's is closing at a location, then future forecasts are adjusted by "y" percent. When I worked at Nordstrom, we loved it when Neiman Marcus moved into a market because existing Nordstrom stores performed better due to Neiman Marcus traffic. This kind of adjustment is baked into the forecast.

Third, profit-and-loss statements are run for each store for each of the next five years. The p&ls are run for each store, and more often these days are run for the "trade area" where a store exists, including online sales.

Fourth, profit-and-loss "what if" statements are run assuming a store is closed. Large retailers know what happens in a trade area if a store is closed. If a store generated $2,000,000 in annual sales and the store is closed, it is common to see 15% of sales move online ($300,000) and 15% of sales move into nearby stores ($300,000) and 70% of sales to simply disappear. Based on historical store closures, "what if" scenarios are run. Between Real Estate / Finance / Analytics, a list of future "unprofitable" trade areas are published. C-Level Executives and Board Members then determine what to do with "unprofitable" trade areas.


How about we work through an example?

Let's assume that we own one store in Portland, Oregon. The store generates $1,500,000 in annual sales, with 25% of sales flowing through to profit, and $150,000 in fixed costs associated with the store. In addition, the online channel in the trade area generates $300,000 in sales, with 25% of sales flowing through to profit and $30,000 of advertising costs (10% of sales) to generate online sales and $30,000 of fixed costs allocated to the online brand in the trade area.
  • Store Profit = $1,500,000 * 0.25 - $150,000 = $225,000.
  • Online Profit = $300,000 * 0.25 - $30,000 - $30,000 = $15,000.
Again, we'll walk through stuff at a very high level. There is a lot more detail that goes into this stuff than I am sharing here.

Forecasts are produced for this store - the models use customer repurchase rates, new customer acquisition count assumptions, and channel crossover assumptions (among other attributes). In our example, let's assume that the store will weaken because sales move online over time.
  • Year 1 Store Sales = $1,500,000 * 0.98 = $1,470,000.
  • Year 2 Store Sales = $1,470,000 * 0.98 = $1,440,600.
  • Year 3 Store Sales = $1,440,600 * 0.98 = $1,411,788.
  • Year 4 Store Sales = $1,411,788 * 0.98 = $1,383,552.
  • Year 5 Store Sales = $1,383,552 * 0.98 = $1,355,881.
Similarly, those sales will move online - and better yet, two things happen. First, online sales will grow at an organic rate and second, online marketing expense will increase as online sales are associated with Facebook Advertising and Paid Search and Affiliates and Regargeting. Let's assume that online sales will grow by the amount that in-store sales decline, and let's assume that online sales grow by 5% organically above-and-beyond current rates. Here's the online forecast for the trade area.
  • Year 1 Online Sales = $300,000 * 1.05 + $30,000 = $345,000.
  • Year 2 Online Sales = $300,000 * 1.10 + $29,400 = $359,400.
  • Year 3 Online Sales = $300,000 * 1.15 + $28,812 = $373,812.
  • Year 4 Online Sales = $300,000 * 1.20 + $28,236 = $388,236.
  • Year 5 Online Sales = $300,000 * 1.25 + $27,671 = $402,671.
Ok, this is where things get interesting. Your Real Estate team and Analytics team will measure the impact of, say, Macy's closing a store in this mall. When Macy's closes, let's assume that sales drop by 3%. And let's assume that other stores will close in future years, hurting retail sales by 3% in the first three years, and then by 5% in years four and five.
  • Year 1 Store Sales = $1,500,000 * 0.98 * 0.97 = $1,425,900.
  • Year 2 Store Sales = $1,425,900 * 0.98 * 0.97 = $1,355,461.
  • Year 3 Store Sales = $1,355,461 * 0.98 * 0.97 = $1,288,501.
  • Year 4 Store Sales = $1,288,501 * 0.98 * 0.95 = $1,199,594.
  • Year 5 Store Sales = $1,199,594 * 0.98 * 0.95 = $1,116,822.
Technically, we need to adjust online sales growth because we have less retail sales, but we'll skip that step for now, as it won't fundamentally change our story.

Make sense so far? Good!

Let's run a profit and loss statement for each of the next five years. We will assume that retail fixed costs and online fixed costs will increase by 3% per year. Ready?

Yup, this market is dying. The store is losing sales to the online channel, and the store suffered because of other store closures in the mall. As a result, profit is in decline.

What does the forecast look like if we close the store? Let's assume that 15% of sales move online, 15% of sales move to other nearby stores, online marketing costs increase as sales move online, and retail fixed costs disappear without the store. What happens?



Look at that! If we close the store, the trade area / market is less profitable. So we're stuck, aren't we? The store is going to perform worse and worse - and yet, profitability is still there, so we have to keep the store open.

This is the process that retailers go through to determine when a store should be closed.

Are there factors that change the relationship I illustrated above, causing stores to close faster or slower? You bet there are!
  • Retail Fixed Costs: Retail brands that are "debt heavy" have greater fixed costs, however, if the store closes, the fixed costs don't go away - they simply hang there and drag on profitability. If a company has minimal debt, then fixed costs disappear, causing the p&l to look better when a store closes, accelerating store closures.
  • Online Marketing Costs: If a retail store closes and sales shift online, and if those sales are then tied to Paid Search and/or Facebook and/or Affiliates and/or Retargeting, then there are added costs that result in a better situation by keeping the store open.
  • Online Fulfillment Costs: Free shipping causes an odd dynamic - variable costs per order increase and as a result the existing store (without the variable cost) becomes more profitable by comparison. In other words, free shipping accelerates customer shifts to the online channel while causing the online channel to appear less profitable and therefore causing Management to keep the existing store open longer.
  • Shift To Online Channel: The faster sales shift to the online channel, the faster you'll close stores. The issue isn't the percentage of sales that are generated online, but instead the rate that sales shift online.
  • New Customer Generation: If the retail store can generate new customers faster than the online channel can generate new customers, then the math will dictate that the store must stay open longer so that the store can fuel online sales growth. This is a little-understood dynamic that only the smartest retail brands take advantage of.
  • Flow-Through to Profit: When a store can generate 40% to 50% flow-through to profit (on a variable basis), the store can stay open longer even when sales shift online quickly. When a store has to discount heavily and can only generate 20% flow-through to profit, then fixed costs quickly overwhelm the p&l causing more profit to be generated by closing the store.
  • Market Cannibalization: The rate that sales move to other stores and/or the online channel dictate whether a store should be closed or not. In a saturated market, it is common for 70% of the sales to remain after a store closes ... half moving to other stores and 20% moving online. Brands that are closing stores (Macy's, JCP, many others) frequently close stores where market saturation is high. When there is only one store in the market, it is common for 15% of the sales to move online and 15% of the sales to move to other stores. Traditional Retail is closing stores because of the interaction between online sales shift and market cannibalization.
  • Product Mix: Certain product lines sell better online than in stores, and vice versa. If the product mix yields comparable rates of sales online vs. in stores, stores are going to close faster. If the product mix skews so that some items sell online better and some items sell in stores better, then there is a place for stores and closing the store will result in the death of product lines, causing long-term online sales to decelerate.
  • In-Store Experience: When you have a Bass Pro Shops or Cabelas, you have an in-store experience that is fundamentally different than walking into a J. Crew. This means it is easier to close a J. Crew store and not lose sales - whereas Bass Pro Shops will get killed when they close a store because those sales are not moving online. If you want to keep your stores open, you need a better-than-average in-store experience.
There are so many other factors as well - factors dependent upon each individual brand. Closing a Nordstrom Rack store cuts off new customers for Nordstrom Full Line stores, which then cuts off Online growth, so you have to put that information into your model, right? Each individual company deals with their own individual challenges.

Does the explanation make sense?

Do you have any questions?

And sure, I do this kind of work, so if you need help give me a holler (kevinh@minethatdata.com).

         
 
 

Sales Down 7%

Some catalog advocates will look to Victoria's Secret ... see a 7% drop ... and then raise a champagne glass in celebration (click here).
  • "See, that proves the POWER of putting paper in the mailbox."
Think about it for a moment. You pull tens of millions of dollars of catalog marketing out of the ecosystem and sales only drop 7%.

Again ... SALES ONLY DROPPED 7% AFTER REMOVING A FULLY-BAKED CATALOG MARKETING PLAN.

If sales dropped by 7% and the ad-to-sales ratio dropped by 10% ... the business is WILDLY more profitable. Now, I have no idea how much paper was buried in the ad-to-sales ratio, but simple math suggests profit had to increase. A 7% sales drop and a 4% ad-to-sales ratio drop would yield an increase in profit.

Please - measure your organic percentage.

Please learn what your business could look like if you changed marketing strategies.

You run these scenarios, right?

         
 
 

Hiding In Plain Sight

In recent years my business shifted ... I used to spend a lot of time analyzing marketing data. These days, I spend a lot of time analyzing the impact of merchandising decisions on business performance.

Why?

I think it comes down to the tools you use to analyze your business.
  1. Your merchandising team typically uses 1980s technology to make decisions (unless you are at H&M or Zara, of course). Even if you have new systems, you are using new systems to produce old metrics.
  2. Your marketing team is obsessed with response/conversion to marketing tactics. The craft has been devalued by Google Analytics - you either pay Adobe or IBM a hundred thousand for advanced analytics or you use Google for free analytics - but make no mistake, none of the tools are designed to help you understand how merchandise performs. If you want to see how 29 Pinterest referrals converted, slice and dice it a thousand ways. If you want to see how 29 new items progressed to winning status? God help you!
We end up looking for solutions in the wrong places. Sales down 5%? Figure out what is wrong with the conversion funnel.

Meanwhile, I'm analyzing merchandising data and the answer is hiding in plain sight.

Do me a favor. It's Friday, and you don't have anything better to do. Here's the query I'm asking for.

Step 1: Identify the year an item was "born".

Step 2: Create variables for each year - sum demand for the item by year. Use 2/24 - 2/23 as your "year", or use calendar year, or use fiscal year, the definition of year isn't that important.

Step 3: Segment items based on the year the item was "born". Count how many items were introduced by year. Then sum demand by year for items "born" in a certain year. If you have five years of data available, you will have five rows in your dataset, and you will have five columns summarizing annual sales totals for each year (row).

Take a look at your table.

You should see what the "life of an item" looks like.

Companies that are struggling discontinue items too quickly, or fail to introduce enough new items, or introduce too many new items that fail to perform well. You'll be able to see the impact of bad decisions over time.

Ok - go run the query. It's easy to run, and many of the issues with your business will be hiding in plain sight.

         
 
 
 
   
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