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Kevin Hillstrom

4 Jul, 9 tweets, 2 min read

1 - Since it is July 4 and you are an analyst awaiting a thrilling afternoon and evening, let's consider the role of data and the role of power in our jobs.

2 - We'll start with the definition of Liberty, since that's always a hot topic.

Liberty: "the state of being free within society from oppressive restrictions imposed by authority on one's way of life, behavior, or political views."

3 - The key phrase there for those of us data-centric folks is "restrictions".

4 - From a data standpoint, most of us are trying to improve sales, improve profitability, or make lives easier/better for other people.

From the position of a person in power, this can be viewed as "restrictions".

When your ideas are viewed as "restrictions", trouble brews.

5 - Let's go back 27 years in my career.

Way WAY back in the day at Lands' End, I had math (#datadriven) that showed that mailing individual catalogs was less profitable than adding the pages to existing catalogs.

In other words, 1 contact a month was better than 3x/month.

6 - Now, my analysis was "data driven".

We executed mail/holdout tests.

We had the facts.

What did business leaders have?

They had power.

My facts impeded their power, limiting the Liberty of the Executive possessing power.

I was "restricting" what that person could do.

7 - Viewed via this framework (restrictions ... limiting Liberty), is it any wonder your #datadriven thesis doesn't go anywhere?

By doing what you tell somebody to do ... even if correct/right ... you put a restriction upon somebody else.

Nobody likes restrictions.

8 - A smart #datadriven analyst positions arguments via opportunity ... not via restriction.

You aren't telling somebody they can't do something.

You're telling somebody that their bonus check increases.

You're telling somebody that THEY look good.

9 - You're telling somebody that they become more powerful.

Don't focus on restrictions ... on reduced Liberty.

Focus on everything that makes the person who must adhere to your data-driven ideas more powerful, happier, better compensated.

Thanks,
KH

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More from @minethatdata

Kevin Hillstrom

@minethatdata

6 Jul

1 - The image below shows a key tenant of the Customer Development Thesis.

When you acquire a customer, you have about three months to convert the customer to a second purchase before the customer fades away.

2 - The curve is virtually identical for most clients. Sure, some clients are better-than-average, some clients struggle (typically because of the product/merchandise offering, not due to marketing failures), but the shape of the relationship almost always looks like this.

3 - When you acquire the customer, the first month is critical. This is the month when the customer is most responsive. Every month thereafter the customer is lapsing, becoming increasingly less interested in a relationship going forward.

Read 9 tweets

Kevin Hillstrom

@minethatdata

21 Jun

This kind of thing happens in retail all the time (i.e. you are the 5th-8th best team out of 30, here's why a writer thinks you are awful: theringer.com/2021/6/21/2254…)

It's easy for a pundit to point out flaws.

Notice that pundits seldom do the hard work (especially in retail)?

In retail (and in e-commerce) the "doing" is really hard.

A pundit might tell you that you just have to be "remarkable" ... that's all ... that's all you have to do.

Have you ever tried to be "remarkable"? The answer is yes. You try doing it every day. It's hard to be consistently "remarkable", isn't it?

If you could be consistent at it. you'd have eight figures in your checking account.

Read 8 tweets

Kevin Hillstrom

@minethatdata

17 Jun

1 - One of the dangers of "consultant communication" is the issuance of the phrase by a professional ... "Your ideas likely won't work because we are unique and we are special, and you probably don't understand our specific business model."

This is a red flag, folks.

2 - This is the way a professional tells you that they aren't going to change.

3 - It means that the professional quite likely agrees with your thesis, or can't find a way to fight your thesis. Without a way to fight your thesis, the professional develops a viewpoint that discredits the thesis ... "we're unique, we're special."

Read 19 tweets

Kevin Hillstrom

@minethatdata

15 Jun

1 - I've noticed that too few e-commerce professionals ... and almost no vendors (and it isn't their job to understand this) understand the fact that if few customers repurchase then the entire focus of the marketing department needs to be on customer acquisition.

2 - You'll miss this point if all you ever look at is conversion rates.

3 - Here's an example I just analyzed, with numbers scaled down to be easily understood.

The brand had 100 twelve-month buyers. 25% of those customers bought again in 2018. The brand then generated 70 new+reactivated buyers.

Next Years Buyer Count: 100*0.25+70 = 95.

Read 8 tweets

Kevin Hillstrom

@minethatdata

14 Jun

1 - This quote is misleading and true.

"Snapchatters spend 1.6x more than the average shopper across ALL Q4 shopping moments."

2 - I'm 100% confident that an analyst queried a database and found this outcome to be true.

3 - It's also a classic misread of a database.

Just because you can query a database doesn't mean you get accurate results. You write a query that is 100% correct and get a result that is bogus.

How so?

Read 7 tweets

Kevin Hillstrom

@minethatdata

13 Jun

1 - Actual e-commerce customer data is indicating that the "COVID-bump" is ending.

This creates all sorts of interesting dynamics that are going to play out over the next two years.

2 - Let's pretend that we have a business where the annual repurchase rate is 30%, there are 70 new customers per year, and there were 100 12-month buyers at the start of COVID. Let's assume that each customer spends $100 a year for simplicity sake.

3 - So, at the end of the year just prior to COVID, we had the following situation.

100 buyers.

Rebuy Rate = 30%.

Existing Buyers = 0.30 * 100 = 30.

New Buyers = 70.

Total Buyers = 30 + 70 = 100.

Spend per Customer = $100.

Total Sales = 100 * $100 = $10,000.

Read 15 tweets

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