Mobile Marketing: New Channel Preference

When a customer or user chooses the new channel as a first interaction with a business, existing channels can struggle to capture the fancy of the customer.

Here's our table. Let's look at the app-only audience.

			April
March	Visit	Use	Website	No
	Website	App	+ App	Activity	Totals
Visit Website	10,000	50	100	20,000	30,150
Use App	100	300	200	1,000	1,600
Website + App	50	250	300	1,000	1,600
No Activity	20,000	400	400	0	20,800
Totals	30,150	1,000	1,000	22,000	54,150

Here's the "Re-Visit Rate" for those who only use the app:

• Re-Visit Rate = (100 + 300 + 200) / (100 + 300 + 200 + 1,000) = 37.5%.

Now, let's calculate the re-visit index for each channel combination:

• Re-Visit Index, Website = 100 / 600 = 16.7%.
• Re-Visit Index, App = 300 / 600 = 50.0%.
• Re-Visit Index, Website + App = 200 / 600 = 33.3%.

The clear preference of the customer is to use only the app. Some customers/users will use both the website and the app.

When you see trends like this, you know that the app is going to win, long-term. And if the app is going to be the preferred channel, long-term, you have a responsibility to educate the rest of your management team about the implications of this shift in customer behavior. The new channel is clearly going to command the interest of the customer, and needs to be developed accordingly. Conversely, the existing channel is going to experience a slow customer drain over time --- if the existing channel is responsible for generating advertising revenue, then a lot of thought needs to go into how your brand will maintain a steady diet of advertising revenue when channel shift really takes hold.

Mobile Marketing: Measuring Channel Shift

Recall our table from last week:

			April
March	Visit	Use	Website	No
	Website	App	+ App	Activity	Totals
Visit Website	10,000	50	100	20,000	30,150
Use App	100	300	200	1,000	1,600
Website + App	50	250	300	1,000	1,600
No Activity	20,000	400	400	0	20,800
Totals	30,150	1,000	1,000	22,000	54,150

When measuring channel shift, always remember the "multi-channel mantra" that is posited by those who manage the incumbent channel: "Multi-channel customers are the best customers".

They aren't the best customers because they are multi-channel, of course. They are already the best customers, and as a consequence, they are often the first to try new channels.

What matter isn't whether they try multiple channels or not, what matters is what they do after trying a new channel. It's what they do after that is dangerous to the incumbent channel.

Look at the table from last week. Let's look at the customers who experience both the website and the app.

• "Re-Visit Rate" = (50 + 250 + 300) / (50 + 250 + 300 + 1,000) = 37.5%.

Now, we'll calculate the "Re-Visit Index" for each channel:

• Re-Visit Index, Website = 50 / (50 + 250 + 300) = 8.3%.
• Re-Visit Index, App = 250 / (50 + 250 + 300) = 41.7%.
• Re-Visit Index, Website + App = 300 / (50 + 250 + 300) = 50.0%

Any index with a value > 20% suggests that the customer wants to change or migrate to other channel combinations. In this case, the customer is not going to be a web-only customer ... the customer is either app-only, or app+website. This is a common trend early in the evolution of a new channel .... customers either switch back to the old channel in the early days, or the customer goes back and forth between channels.

Pay close attention to the fact that the app index is five times greater than the website index (41.7% to 8.3%). This is a strong indication that the customer prefers the app over the website.

Mobile Marketing: Measuring Channel Shift

Multichannel Forensics are ideally suited to help us understand what impact mobile marketing might have on the future.

Let's say that you have a mobile app, one that you launched on March 1. Do this for me.

Step 1: Segment users into one of three groups during March. Group 1 = Visit Website. Group 2 = Use App. Group 3 = Website + App.

Step 2: Segment users into one of three groups during April. Group 1 = Visit Website. Group 2 = Use App. Group 3 = Website + App.

Now, what you want to do is create a two-way table that counts how many users fell into each group in March, and then again during April. Here's an example:

			April
March	Visit	Use	Website	No
	Website	App	+ App	Activity	Totals
Visit Website	10,000	50	100	20,000	30,150
Use App	100	300	200	1,000	1,600
Website + App	50	250	300	1,000	1,600
No Activity	20,000	400	400	0	20,800
Totals	30,150	1,000	1,000	22,000	54,150

This table forms the basis for our study of channel shift. I'll give you six days to think about this table. What do you observe happening here with app users?

Next week, we'll explore the meaning of the numbers in the table.

Mobile Marketing: Channel Shift

Fear.

That's what I hear when I talk to the e-commerce generation about mobile marketing.

It's almost like there isn't any fear regarding Social Media, like that's a harmless thing that is fun to measure and be part of, but not something that will replace traditional e-commerce.

Mobile Marketing is a different story.

Time spent on a mobile device may be incremental time that would not have been spent interacting with the internet five years ago. Conversely, a ton of time spent on a mobile device today is time that would have been spent on a laptop or a desktop computer.

And when you spend time in a different way, you begin to interact with different things. Instead of visiting a website, you use an app to interact with a brand. Once the individual uses the app a few times, the relationship between the individual and the traditional website is fundamentally changed.

This is what frightens the e-commerce generation.

What if the customer stops interacting with the traditional website?

Our job, of course, is to validate customer behavior. We determine if the customer "adds" mobile to the total experience (this is what the e-commerce generation wants to see happen), or we want to determine if the customer "shifts" away from e-commerce, toward the mobile experience.

When the customer is shifting away from e-commerce, we have a responsibility to look into the future. It becomes our job to predict how this shift impacts each channel, and once we visualize this shift, we need to educate folks, and chart a path to the future.

Mobile Marketing: Lift

Now that we understand how our mobile app shoppers are distributed across our customer file, it is our job to begin to measure the incremental "lift" we expect from these customers going forward.

When file counts are small, we're just kind of stuck having to use what we have.

Remember, in yesterday's example, most of the mobile customers were graded as "A".

	Total	Mobile
	File	File	Index
Grade = "A"	50,000	394	2.58
Grade = "B"	50,000	176	1.15
Grade = "C"	50,000	88	0.58
Grade = "D"	50,000	65	0.42
Grade = "F"	50,000	42	0.27
Totals	250,000	765

So here's what we can do. Let's take everybody at the end of March who had a grade of "A". Split that segment by those who used a mobile app recently, and those who did not.

Now, for each group, measure the incremental demand generated during the month of April. Your table should look something like this:

							Non-
						% Via	Mobile
		HHs	Rebuy	Spend	Value	Mobile	Value
Grade = A, Mobile		394	3.4%	$165.00	$5.61	17.5%	$4.63
Grade = A, Other		50,000	3.1%	$170.00	$5.27	0.4%	$5.25
Lift					6.5%		-11.8%

There are a couple of things worth noticing here. First of all, the mobile app buyers, after equalizing a bit for customer quality, are worth 6.5% more in the month of April than are other customers. That's what we want to see, we want to see that the new channel creates value among customers.

Also notice that the number is 6.5% ... it's a small number. In cases like this, don't expect mobile apps to be the life saver you've been looking for to generate huge sales increases (unless the percentage of mobile app users are disproportionately new customers).

Finally, notice that non-mobile volume is nearly 12% less among mobile app shoppers. If you see an outcome like this, then you know that your efforts are causing these customers to shift business from your core website to the mobile app ... in other words, your online sales are being cannibalized by the mobile app.

Cannibalization matters, folks. Most businesses miss cannibalization until it is too late. Just ask catalogers, just ask the newspaper industry, just ask independent record stores. When sales are cannibalized, you have strategic issues that need to be addresses, so that "channel shift" is not devastating to your business.

Ok, you've read several posts about measuring fundamental relationships in mobile marketing, what are your thoughts, what questions do you have? We've got more posts coming ... that being said, this would be a good time for input.

Mobile Marketing: Loyalists

Once you've identified the mix of new/existing mobile app users, the next step is to identify how loyal these customers/users have been, historically.

In e-commerce, I like to take my customer file, and split it into six segments. Any customer without a purchase in the last twelve months, but at least one life-to-date purchase is put into segment "Z".

Then, I use dollar cutoff points to rank customers based on twelve month activity. For any customer who purchased in the past twelve months, I create five equal cutpoints. Here's an example:

• Spend $500+ = "A"
• Spend $300 to $499 = "B"
• Spend $200 to $299 = "C"
• Spend $100 to $199 = "D"
• Spend $1 to $99 = "F"

I try to use cutpoints to make sure that there are reasonably equal numbers of customers in each "grade".

Now that you've done this exercise, look at all mobile app purchasers in the past twelve months, and look at total dollars in the past year. Compare the distribution to the entire file:

	Total	Mobile
	File	File	Index
Grade = "A"	50,000	394	2.58
Grade = "B"	50,000	176	1.15
Grade = "C"	50,000	88	0.58
Grade = "D"	50,000	65	0.42
Grade = "F"	50,000	42	0.27
Totals	250,000	765

In this example, it is obvious that your mobile shoppers are disproportionately skewed to the top portion of your twelve month buyer file. This means that your mobile shoppers are likely to be disproportionately loyal to your business.

When this happens, the question of "incrementality" comes into play. Your most loyal buyers are often the ones most likely to try new channels, and when they try a new channel, they are not necessarily interested in purchasing more, they are simply trying a new channel.

So that's not so hard, is it? Give the methodology a try. It's basic, simple, and it allows you to clearly communicate results to management without your leaders becoming paralyzed by methodology issues.

Mobile Marketing: Newbies

Let's assume that you have your mobile marketing strategy in place. Maybe you built an app, and are taking live, actual orders. You are ready to get started.

There are plenty of good sources for measuring the basics of mobile marketing, so we aren't going to focus on basic metrics readily available in your web analytics application.

The very first metric worth calculating is new customers.

You already calculate simple metrics for your other channels.

• You know that 42% of all purchasers in April came were first time buyers.
• You know that 47% of offline/catalog purchasers in April were first time buyers.
• You know that 50% of branded paid search purchasers in April were first time buyers.
• You know that 61% of non-branded paid search purchasers in April were first time buyers.
• You know that 38% of organic search purchasers in April were first time buyers.
• You know that 19% of e-mail purchasers in April were first time buyers.
• You know that 40% of affiliate purchasers in April were first time buyers.

You know those things, right?

So the very first thing you do with any customer purchasing from a mobile app (or social media for that manner) is you identify how many of the customers are new, and how many are existing.

If, by comparing the numbers above, you find that 11% of your app buyers are new buyers, well, then there's going to be some fun analysis, right? We are going to have to determine whether the buyers would have purchased anyway, or if the app captured demand that would not have happened otherwise.

If, by comparing the numbers above, you find that 67% of your app buyers are new buyers, well, then you have something magical happening in the early stages of the channel.

Start simple. And communicate simple facts to your Executive Team. Stay away from making this stuff more complex than it has to be ... because as we work through this series, things are going to become more complex, and the more complex things become, the less people trust you!

Mobile Marketing: Incrementality

Many of you are now peppering me with questions about mobile marketing.

No, not questions about whether it works or not, or how to create an app. Instead, you are asking whether any demand attributed to a mobile app is "incremental".

The concept of "incrementality" is an old one. Back in the early 1990s, catalogers raced to maximize demand by mailing as many catalogs as possible. The goal, of course, was to find the number of catalogs that generated the most incremental profit. Some catalogs generated no incremental demand, if you didn't mail the catalog, the customer spent just as much.

An entire generation of direct marketers, now age 40-55, earned their stripes in this area of expertise. I count myself in this camp. This camp thinks very differently than other generations of direct marketers.

The online generation, folks who earned their stripes between 1995 and 2005, do not think in terms of incrementality. These folks never had to think in these terms, because their craft cannibalized sales from existing channels. They fought the "catalog generation", the folks who were being cannibalized, the folks who spent way too much time measuring the incremental value of the internet.

The online generation is about to care, to really care, about incrementality.

Tablet devices (iPad) will spawn micro-sites that are directionally linked to an e-commerce website, but are technically different ... just like the 18th catalog offering targeted merchandise was fundamentally different from the core catalogs mailed by a brand in 1992.

Mobile devices change purchase patterns. You walk into a Talbots store to buy a dress, but they only offer core sizes ... so you punch up your Talbots app to buy the extended size. This behavior makes you fundamentally different than the average customer, for at least two reasons.

1. You are a technology shopper, vs. the non-app shopper.
2. You buy extended sizes not available in stores, making you different than the core size shopper.

Is this purchase incremental? Maybe. Are future behaviors, now changed because of this shift from a store purchase to an app purchase, worth more? Maybe. Will the customer visit the e-commerce website, given the app purchase? Maybe.

None of these questions are answered by allocation models. They can be answered via Online Marketing Simulations and Multichannel Forensics.

We are going to spend time in May talking about the incremental value of a mobile channel. It seems that this is worth discussing, given your feedback. And almost nobody addresses the topic, so it is worth considering.