Forecast Forensics + Digital Profiles: Time To Act!

Well, we've covered a lot of ground, haven't we?

We created a modern segmentation system, called "Digital Profiles", a system that allows us to understand complex customer behavior.  We mapped the results, so that we can understand how customers move through our marketing ecosystem.

Our Digital Profiles can be used to forecast the future.  This allows us to craft a scenario that meets Management expectations.

We end up with an actionable analysis that helps us understand how to grow a business.

I mean, you can do a lot of guesswork analyzing whether marketing campaigns work or do not work, or you can be a leader, helping your company understand what it takes to grow, measuring whether you are making progress against stated growth metrics.

You'll learn that CEOs, CFOs, and CMOs really like this style of analysis, because for once, they get to actually see what needs to be done to truly grow a business.

It's time to act!

Start your own project.  I've given you all of the tools you need to do a project like this.  Get busy!

Or, contact me now and I'll create your own customized project for you.  This project is very popular among CEOs, CFOs, and CMOs.

Forecast Forensics + Digital Profiles: Case Study

Let's say that your Management team is being asked to grow the business over the next five years.  The mandate is simple ... the business must be 2.5 times as big in 2015 as it was in 2010.

Oh boy.

Well, somebody might say ... "I'll bet if we can increase customer loyalty by 20%, we'll be able to grow this business.  Let's see what impact that has:

Good gravy, that doesn't get the job done, does it?  Now, be honest, there's nothing wrong with the business being 33% bigger in year five because annual repurchase rates increased from the mid-40s to the mid-50s.  That's a big home run.

But it isn't what Management is asking for, is it?

The answer has to come from new customer acquisition.  Let's try something.  Let's reset loyalty metrics to base levels, and let's double new customer acquisition counts.

Now, be honest, it isn't easy to double customer acquisition counts, is it?  And yet, if you double counts every year, it isn't good enough!  It isn't good enough!


How about a 2.83x increase in new customers?

That does it!


Your job is to make this message easy for everybody to understand.  You are going to have to, essentially, triple the number of new customers, in order to grow the business by a factor of 2.5x.


Dashboard mavens will love this ... you set up dashboards for everybody to see, illustrating the number of new customers vs. the goal of tripling current totals, by source.  Everything gets measured.


Profit should get measured, too.  If it costs you $10 of profit to acquire an incremental new customer today, and it will cost you an incremental $25 of profit to acquire a new customer in this new scenario, well, you know exactly how much money you need to ask for to make this happen, right?!


That's how we use Forecast Forensics in combination with Digital Profiles.  We instantly see what is required to grow the business.  We, as analytical experts, guide our CEO, CFO, and CMO toward tangible, actionable solutions.


This is an analytics system, a Segmentation/Forecasting system, that yields actionable outcomes.

Forecast Forensics + Digital Profiles: Impact Of Loyalty

Let's say that, somehow, you find a magical formula that allows you to increase loyalty for just one year, by 10%.

What impact does that effort have?

Well, you get $1.9 million in demand in the year where the improvement happens.

But you also cause more customers to purchase, and those customers act like "compound interest".

In year two, demand is $0.8 million greater.

In year three, demand is $0.6 million greater.

In year four, demand is $0.3 million greater.

In year five, demand is $0.2 million greater.

So you get $1.9 million from a one year, 10% increase in loyalty ... and you get $1.9 million in years two through five ... compound interest!

Now, if you have some magic formula for improving customer loyalty, well, you'd already be implementing the strategy, right?  I mean, you wouldn't hold that in your pocket so that you could use it three years from now!!

But if you stumble across something, rest assured that you get the short-term benefit of the strategy, and you get a "compound interest" effect as well.

Forecast Forensics + Digital Profiles: The Importance Of New Customers

Ever read these quotes about keeping your best customers?

• "Customer loyalty is the most important factor in the success of a business."
• "Brands that lead the loyalty game lead the market share game".
• "It costs eight times as much to recruit a new customer as it costs to keep an existing customer."
• "You, too, can turn your best customers into faithful brand evangelists."
• "Fourteen quick tips to launch your brand into loyalty heaven."

There are two things you learn when you use Forecast Forensics to understand your business.

1. You don't improve customer loyalty, on a long-term basis, by "running a campaign".  In fact, loyalty seldom changes significantly, from year-to-year, and when it does improve for a few years, it is met with a one or two year decrease that resets loyalty back to historical levels.  Seriously, this is true.  Measure it sometime!
2. We dramatically underestimate the importance of acquiring new customers.

Look at the image above.  This is the business we've been analyzing for the past month.  In this scenario, I decided to cut off new customer acquisition.  In other words, for the next five years in this simulation, I did not allow the business to acquire one single new customer.

Annual demand drops from $26 million to $18 million in just one year.  And it keeps getting worse, by year five, demand is just $4 million.

I'm not saying you shouldn't worry about customer loyalty.  You should!

I am saying that, proportionately, customer loyalty is a three on a scale from one to ten, while new customer acquisition is an eight on a scale from one to ten.

And because nobody talks about new customer acquisition, there is a business opportunity awaiting you ... your competition doesn't understand just how important this topic is. Take advantage of the opportunity that is in front of you.

Forecast Forensics + Digital Profiles: Triple E-Mail Frequency

With the impact of advertising built into the spreadsheet, we can simulate a veritable plethora of scenarios.

For instance, we can experiment with a tripling of the e-mail marketing frequency.  Click on the image below to see what happens to the growth rates of each Digital Profile, when the e-mail marketing frequency is tripled.

Look at the Digital Profiles associated with e-mail marketing ... those profiles grow at a very healthy rate, don't they? E-mail Loyalists grow by 132% over five years, E-mail Plus Search grows by 98% over five years, Adores E-mail grows by 108% over five years.

Notice that other segments grow, some a lot (Mobile Mavens), some not so much (Pricey Website Preference).  In other words, the type of marketing you choose to employ can play a role in how your business evolves over time (hint --- discounts and promotions).

Take a look at what happens to sales, over the course of the next five years.

Retention rates are clearly increased, causing the business to grow significantly over time.  Notice the compound impact that happens, sales grow additionally each year, because more customers are retained, causing more customers to exist.

If you'd like to have these scenarios run for your business, give me a holler, I'd be happy to help.

Forecast Forensics + Digital Profiles: Making Adjustments

If we can predict the Digital Profile that a customer is likely to migrate to, and if we can predict how much a customer is likely to spend, then we can make adjustments to our predictions, allowing us to see how a changing business might lead to a changed business in the future.

I like to create a tab in a worksheet that allows me to make changes to the future trajectory of the business.  My tab looks something like this:

Take a look at the image above.  In the bottom half of the worksheet, I change how customer acquisition is likely to evolve, in the future.  If a Digital Profile shows a historical increase of, say, 15%, then I might type in a factor of 1.15, to reflect a future 15% increase.

Take a look at the "Mobile Mavens" row ... I've jacked that one way up, to account for projected increases in the channel.  Conversely, I ratcheted down the "Pricey Website Preference" Digital Profile, in order to account for a likely shift in customer behavior, going forward.

If we can predict where a customer is likely to migrate to ... and if we can predict how much that customer might spend ... and if we can make adjustments, allowing us to consider different possible outcomes ... well, then, we've got something interesting, don't we?

Next week, we'll explore some of the possible outcomes offered by the combination of Forecast Forensics and Digital Profiles.

Forecast Forensics + Digital Profiles: Demand Forecast

If we can predict the Digital Profiles that a customer will belong to in the future, then we can easily predict key sales metrics, right?

For instance, if we have 1,000 customers, and 40% of the customers purchase again, and 10% of customers migrate to the Mobile Mavens segment, and each Mobile Mavens customer spends $249.55, well, then we can easily calculate what is likely to happen.

• 1,000 * 0.40 * 0.10 * $249.55 = $9,982.

If I can do this for one segment / Digital Profile, I can do it for any Digital Profile.  And, I can do it for new customers as well, projecting how many new customers I'm likely to obtain by Digital Profile.

So, we can predict the Digital Profiles a customer is likely to migrate to, and we can predict the amount a customer is likely to spend.

Seems like that should be pretty useful, if you're a CEO, CFO, or a Chief Marketing Officer, right?

Forecast Forensics + Digital Profiles: Forecasting

Today is a transition day ... we're moving away from the segmentation part of this series, moving to the forecasting part of this series.

Recall, Forecast Forensics is all about using conditional probabilities to illustrate how customers are likely to migrate between segments.

In other words, if we have a customer that belongs in the "Web Masters" segment, we can calculate how likely that customer is to purchase again next year ... and if the customer purchases again, we can calculate the probability of a customer migrating to any one of the sixteen Digital Profiles.


And if we can do that for one year, well, we can do that out into infinity, or at least for five years, right?


And if we can do that, well, then we can literally "see" what the future might look like.


So take a look at the image.  This is the forecasted count of twelve-month buyers by Digital Profile, for each of the next five years.


Tell me what you see.

Forecast Forensics + Digital Profiles: Growth And Decline

Before we get to spreadsheet-intensive simulations, let's take a quick look at how counts within each Digital Profile evolved over the past three years (click here to contact me for your customized Forecast Forensics + Digital Profile project).

The red lines represent the most valuable Digital Profiles.  The biggest growth profile is "The Future of Multi-Channel", a profile that includes significant Mobile and Social purchases.  Declines are in the older-school e-commerce and multi-channel segments.  It may well be that cannibalization is underway, given that these profiles possess comparable value.


The biggest level of growth happened in "Mobile Mavens", a mid-value Digital Profile inhabited by mobile shoppers.


Catalogs Are Dead is growing at a significant rate, recall that this is a pure e-commerce Digital Profile.  Couple this with declines in Cheap Catalog Items and Pricey Catalog Items, and we may be seeing the continued evolution of the customer base.  Also notice that Web Masters and Pricey Website Performance are in decline.  This is a customer file that is evolving in stages, away from catalog to e-commerce, then away from e-commerce to emerging channels.


This is stuff you probably want to know about your business, right?

Forecast Forensics + Digital Profiles: How Channels Fit Together

Each of our Digital Profiles are defined.  Now let's see how channels fit together (contact me if you'd like to have your own custom Forecast Forensics + Digital Profiles project).

I run a query, identifying the top three Digital Profiles that customers in each Digital Profile migrate to in the subsequent year.  This relationship yields the following relationship (click to enlarge):

Oh boy.  OH BOY!

The secrets of your business are unlocked, they are visually apparent in this image.

New customers migrate in from the upper left hand corner of the image.  Loyal customers are in the bottom right hand corner of the image.

Newbies filter into lower-value segments, like Adores Email, Pricey Website Preference, Web Masters, and Gaga For Google.  In other words, the online channel is the primary source of acquisition for this brand, with e-mail and search playing a role in acquiring new customers.

Take a look at the upper right hand corner of the image.  Here's where the "catalog" portion of this business exists ... it is almost separate from the rest of the business, isn't it?  Customers can get to this part of the business through The Future Of Multichannel profile, and customers in this quadrant can become loyal customers (Classic Multichannel).  By and large, however, the catalog portion of this business is separated a bit from the rest of the ecosystem.  Pay attention to your own business ... if you notice this within your business ecosystem, well, that says something about the future of your business, and your downstream marketing strategy, doesn't it?

There are five Digital Profiles that yield loyal customers.

• Classic Multichannel (from the catalog ecosystem).
• Search and Shopping.
• The Future of Multichannel (including mobile & social buyers).
• Email Loyalists.
• Crazy for E-commerce. 

Notice that one profile includes the catalog ecosystem, one includes the e-commerce ecosystem, one includes the e-mail ecosystem, one included just about everything, and one includes search, too.  This business can yield high-value customers from a single channel, or from multiple channels.

The Digital Profile process yields several strategic questions.

• Is the catalog ecosystem separate from the rest of the customer ecosystem because the channel is dying, because it is unique and interesting to a subset of customers, or because the brand failed to integrate it properly?
• Social customers are surprisingly mainstream, among the better portion of the customer file?  It does not look like this is a big customer acquisition channel.  What value does a social channel deliver to a brand if it is skewed to best customers?
• Mobile customers are also surprisingly mainstream.  Does this mean that e-commerce will be cannibalized or enhanced, going forward?
• Search customers seem to be linked to e-mail customers.  Does e-mail marketing cause a customer to conduct a search, and if so, does e-mail marketing actually encourage customers to shop the competition? 

Forecast Forensics + Digital Profiles: The 16 Profiles

Last week, we introduced the concept of marrying Forecast Forensics and Digital Profiles (contact me for your own customized project).  This week, we get to see what the geeky math created for us!


Let's review the attributes of each of the sixteen Digital Profiles we generated.


Digital Profile #1 = The Future Of Multi-Channel

• 2,278 households.
• $642.25 spend last year.
• $40.10 price per item.
• 39% shop via Telephone.
• 13% shop Online.
• 74% shop E-Mail.
• 27% shop via Search.
• 15% shop via Social.
• 41% shop via Mobile.
• This customer has a propensity for shopping via any channel, including Social and Mobile, representing the future of multi-channel e-commerce.

Digital Profile #2 = Classic Multi-Channel

• 4,483 households.
• $735.11 spend last year.
• $43.24 price per item.
• 87% shop via Telephone.
• 60% shop Online.
• 20% shop E-Mail.
• 1% shop via Search.
• 15% shop via Social.
• 13% shop via Mobile.
• This is the classic situation, with the customer shopping via the phone and online.

Digital Profile #3 = Mobile Mavens

• 3,344 households.
• $249.55 spend last year.
• $64.62 price per item.
• 16% shop via Telephone.
• 8% shop Online.
• 13% shop E-Mail.
• 25% shop via Search.
• 21% shop via Social.
• 67% shop via Mobile.
• This Digital Profile is most likely to shop via the Mobile channel.

Digital Profile #4 = Social Mom & Dad

• 3,309 households.
• $374.61 spend last year.
• $80.44 price per item.
• 72% shop via Telephone.
• 41% shop Online.
• 2% shop E-Mail.
• 0% shop via Search.
• 36% shop via Social.
• 10% shop via Mobile.
• This customer is most likely to shop via Social, and it turns out that nearly 3/4th of this audience also shop via Telephone, suggesting this is an older audience.

Digital Profile #5 = E-Mail Loyalists

• 5,938 households.
• $692.09 spend last year.
• $41.58 price per item.
• 0% shop via Telephone.
• 85% shop Online.
• 95% shop E-Mail.
• 18% shop via Search.
• 9% shop via Social.
• 11% shop via Mobile.
• Notice that the customer also shops online ... with none of the customers shopping via the telephone.

Digital Profile #6 = Crazy For E-Commerce

• 2,026 households.
• $719.99 spend last year.
• $39.99 price per item.
• 0% shop via Telephone.
• 100% shop Online.
• 1% shop E-Mail.
• 0% shop via Search.
• 21% shop via Social.
• 26% shop via Mobile.
• An e-commerce customer with a propensity for shopping via emerging channels.

Digital Profile #7 = Searching And Shopping

• 2,078 households.
• $582.75 spend last year.
• $70.89 price per item.
• 0% shop via Telephone.
• 93% shop Online.
• 48% shop E-Mail.
• 72% shop via Search.
• 14% shop via Social.
• 16% shop via Mobile.
• Anytime you see this distribution, you worry a little bit ... the customer is brand loyal, but may not be achieving her potential due to a propensity to shop via Search.

Digital Profile #8 = Catalogs Are Dead

• 2,629 households.
• $438.68 spend last year.
• $72.46 price per item.
• 0% shop via Telephone.
• 100% shop Online.
• 0% shop E-Mail.
• 0% shop via Search.
• 35% shop via Social.
• 39% shop via Mobile.
• This customer does not fit the catalog profile (0% via phone).  Granted, the customer could shop online via catalogs, but given the high percentages associated with Social and Mobile, it's more likely this customer is aligned with emerging channels.

Digital Profile #9 = E-Mail Plus Search

• 4,214 households.
• $272.63 spend last year.
• $34.99 price per item.
• 6% shop via Telephone.
• 0% shop Online.
• 70% shop E-Mail.
• 36% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• This is obviously a discount-type shopper, with a low price per item, shopping online.

Digital Profile #10 = Cheap Catalog Items

• 7,380 households.
• $243.21 spend last year.
• $37.74 price per item.
• 100% shop via Telephone.
• 10% shop Online.
• 0% shop E-Mail.
• 0% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• Another budget customer buying low price-point items.

Digital Profile #11 = GaGa For Google

• 6,652 households.
• $146.07 spend last year.
• $71.59 price per item.
• 3% shop via Telephone.
• 0% shop Online.
• 7% shop E-Mail.
• 94% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• A one-time or two-time per year buyer using Google to place the order.

Digital Profile #12 = Pricey Catalog Items

• 7,168 households.
• $142.02 spend last year.
• $74.92 price per item.
• 100% shop via Telephone.
• 2% shop Online.
• 0% shop E-Mail.
• 0% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• Pure catalog shopper buying expensive items.

Digital Profile #13 = Adores E-Mail

• 9,832 households.
• $152.68 spend last year.
• $37.34 price per item.
• 0% shop via Telephone.
• 28% shop Online.
• 97% shop E-Mail.
• 5% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• E-mail shopper who enjoys buying inexpensive items.

Digital Profile #14 = Web Masters

• 12,014 households.
• $195.17 spend last year.
• $33.66 price per item.
• 0% shop via Telephone.
• 100% shop Online.
• 0% shop E-Mail.
• 0% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• Online buyer who purchases inexpensive items.

Digital Profile #15 = Pricey Digital Preference

• 3,040 households.
• $184.88 spend last year.
• $71.40 price per item.
• 0% shop via Telephone.
• 44% shop Online.
• 63% shop E-Mail.
• 43% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• E-mail shopper who buys online and buys pricey items.

Digital Profile #16 = Pricey Website Preference

• 17,345 households.
• $130.79 spend last year.
• $74.29 price per item.
• 0% shop via Telephone.
• 100% shop Online.
• 0% shop E-Mail.
• 0% shop via Search.
• 0% shop via Social.
• 0% shop via Mobile.
• Expensive items, only purchased online.

Forecast Forensics + Digital Profiles: Converting Factors

Here's where the rubber meets the road, as they say!

Yesterday, we created four factors.  Today, we assign customers to one of sixteen Digital Profiles.

WARNING:  Geeky math alert ... feel free to skim if you don't like math!

We score each customer, by standardizing each variable (remember, yesterday we calculated the mean and standard deviation of each variable on a twelve-month basis), then we multiply the coefficients in the Component Score Coefficient Matrix by the standardized variables.  Here's the Component Score Coefficient Matrix:

At this point, I have four factors.

Next, if a factor has a value greater than or equal to zero, we assign a value equal to one, otherwise zero.  Once we do this, we combine four factors by two values each, yielding sixteen Digital Profiles.  Here's my SPSS code, if you're interested (the four factors are f1, f2, f3, and f4).

compute d1 = -0.0000.
compute d2 = -0.0000.
compute d3 = -0.0000.
compute d4 = -0.0000.
compute dp =  00.
if (f1 ge d1) and (f2 ge d2) and (f3 ge d3) and (f4 ge d4)  dp = 01.
if (f1 ge d1) and (f2 ge d2) and (f3 ge d3) and (f4 lt d4)  dp = 02.
if (f1 ge d1) and (f2 ge d2) and (f3 lt d3) and (f4 ge d4)  dp = 03.
if (f1 ge d1) and (f2 ge d2) and (f3 lt d3) and (f4 lt d4)  dp = 04.
if (f1 ge d1) and (f2 lt d2) and (f3 ge d3) and (f4 ge d4)  dp = 05.
if (f1 ge d1) and (f2 lt d2) and (f3 ge d3) and (f4 lt d4)  dp = 06.
if (f1 ge d1) and (f2 lt d2) and (f3 lt d3) and (f4 ge d4)  dp = 07.
if (f1 ge d1) and (f2 lt d2) and (f3 lt d3) and (f4 lt d4)  dp = 08.
if (f1 lt d1) and (f2 ge d2) and (f3 ge d3) and (f4 ge d4)  dp = 09.
if (f1 lt d1) and (f2 ge d2) and (f3 ge d3) and (f4 lt d4)  dp = 10.
if (f1 lt d1) and (f2 ge d2) and (f3 lt d3) and (f4 ge d4)  dp = 11.
if (f1 lt d1) and (f2 ge d2) and (f3 lt d3) and (f4 lt d4)  dp = 12.
if (f1 lt d1) and (f2 lt d2) and (f3 ge d3) and (f4 ge d4)  dp = 13.
if (f1 lt d1) and (f2 lt d2) and (f3 ge d3) and (f4 lt d4)  dp = 14.
if (f1 lt d1) and (f2 lt d2) and (f3 lt d3) and (f4 ge d4)  dp = 15. 
if (f1 lt d1) and (f2 lt d2) and (f3 lt d3) and (f4 lt d4)  dp = 16.

With this logic ... and the instructions from above, I have sixteen Digital Profiles.

Next Week:  We describe each of the sixteen Digital Profiles that will be used in our Forecast Forensics analysis!  Contact me if you'd like to have your own customized Forecast Forensics / Digital Profiles analysis.

Forecast Forensics + Digital Profiles: Creation Via Factor Analysis

Here's the variables that I elected to enter into creation of sixteen Digital Profiles (contact me for your own customized project):

• Data for the past twelve months ... using the scoring algorithm from the past twelve months to score prior years as well.
• Frequency:  Orders in past year.
• Items per Order:  Total annual items (20) divided by total annual orders (4) = 5.00.
• Price per Item:  Total annual demand ($800) divided by total annual items (20) = $40.00.
• 1/0 Indicator:  Did customer buy using telephone channel in past year?  1 = yes, 0 = no.
• 1/0 Indicator:  Did customer buy using all other online channels in past year?
• 1/0 Indicator:  Did customer buy using last-click attribution to e-mail in past year?
• 1/0 Indicator:  Did customer buy using last-click attribution to search in past year?
• 1/0 Indicator:  Did customer buy using last-click attribution to social media in past year?
• 1/0 Indicator:  Did customer buy using last-click attribution to mobile in past year?

So, we create a dataset that has one year of data, with these attributes.

WARNING:  The rest of this post gets really "geeky" ... so if you don't like math, move along, there's nothing to see here!


Here are descriptive statistics for our variables:

The means and standard deviations are used later, when I want to create each of four factors.


Next, we run a factor analysis / principal components analysis, extracting four factors.  Here is the rotated component matrix:

In this analysis, we're looking for metrics with an absolute value greater than 0.20 ... this helps us identify the variables that contribute to each factor.

• Factor #1 = Frequency, Mobile, and Social.  This factor likes loyal customers who have migrated to mobile and social channels.
• Factor #2 = Telephone, Not Online.  In other words, this factor favors old-school shoppers who call the contact center to place an order.
• Factor #3 = Many Items per Order, Low Price per Item:  These customers like cheap items, and they buy lots of cheap items!
• Factor #4 = E-Mail + Search:  Customers who buy via e-mail and search, not necessarily other online channels, fall into this factor.  Kinda makes one wonder if e-mail causes search to happen, doesn't it?

Up Next:  We'll create sixteen Digital Profiles from the four factors extracted from this analysis.

Forecast Forensics + Digital Profiles: A Marriage Made In Heaven!

It's time to combine two fantastic methodologies, yielding a highly robust framework for understanding just what the heck is happening in your business!

Over the next several weeks, I will combine Forecast Forensics with Digital Profiles, illustrating how a multi-channel business has a customer base that his moving in many different directions, all at the same time!

Sound like the business you're managing?  Probably!


Tomorrow, we get started.  The database I'm using has several years of purchase history.  I will analyze six key channels:

• Telephone:  Customers ordering via the phone, primarily from catalogs.
• Online:  Pure online orders, minor online channels (affiliates), online orders driven by catalogs.
• E-Mail:  Orders with last click attributed to e-mail.
• Search:  Orders with last click attributed to search.
• Mobile:  Orders with last click attributed to mobile app or mobile website.
• Social:  Orders with last click attributed to social media.

I can hear some of you grumbling already, bemoaning the fact that I'm using last click attribution in this series.  Well, why don't you use this opportunity to take the methodology I'll share with you, and apply your attribution system to this methodology?  Better yet, why don't you freely publish the results so that everybody can benefit?


So, we'll take six channels, and we'll explore how customers are segmented (using Digital Profiles).  Then, we'll use the Forecast Forensics methodology to illustrate how the business is likely to evolve in the future, given what we've learned about customer behavior.


Forecast Forensics + Digital Profiles:  A marriage made in heaven!

Forecast Forensics: Are Channels Complimentary?

Let me ask you a question:

• "For every dollar you increase the amount of demand you generate in Search, how much additional demand do you generate in all of your other advertising/physical channels?'

Can you answer that question?

If not, why not?  I mean, that's about as basic a question as you're ever going to run across, right?

The Forecast Forensics framework can be used to answer these questions.  After all, all of this multichannel stuff says that channels work together in a multiplicative way to grow our businesses exponentially, right?

So, I ran the framework for data I have.  I ran four simulations, across five years.

• Simulation #1 = Base Case.
• Simulation #2 = Grow Telephone Volume by $1.5 Million in Year Five.
• Simulation #3 = Grow Search Volume by $1.5 Million in Year Five.
• Simulation #4 = Grow Mobile Volume by $1.5 Million in Year Five.

Let's look at how other channels grow, when these channels grow.

Grow Telephone Demand By $1.5 Million In Year Five:

• Telephone = Increase of $1.5 Million.
• Online = Increase of $0.2 Million.
• E-Mail = Increase of < $0.1 Million.
• Search = Increase of < $0.1 Million.
• Social = Increase of < $0.1 Million.
• Mobile = Increase of < $0.1 Million.

Grow Search Demand By $1.5 Million In Year Five:

• Telephone = Increase of $0.2 Million.
• Online = Increase of $1.0 Million.
• E-Mail = Increase of $0.5 Million.
• Search = Increase of $1.5 Million.
• Social = Increase of $0.1 Million.
• Mobile = Increase of $0.1 Million.

Grow Mobile Demand By $1.5 Million In Year Five:

• Telephone = Increase of $0.3 Million.
• Online = Increase of $1.0 Million.
• E-Mail = Increase of $0.7 Million.
• Search = Increase of $0.3 Million.
• Social = Increase of $0.1 Million.
• Mobile = Increase of $1.5 Million.

In other words, each channel yields a different result.

• Growing Telephone orders (i.e. sending catalogs to rural customers) results in a simple increase in the Telephone channel, without helping other channels.  Each Telephone dollar adds $0.20 across other channels. Catalog orders over the phone are not complementary to other channels, in this case.
• Growing Search orders results in $1.5 million in Search and $1.9 million in other channels.  Each Search dollar you add results in $1.27 being added to other channels, as well.  Search is complementary to other channels, in this case.
• Growing Mobile orders results in $1.5 million in Mobile and $2.4 million in other channels.  Each Mobile dollar you add results in $1.60 being added to other channels, as well.  Mobile is complementary to other channels, in this case.

As a marketer, you probably want to know this fact, right?  I mean, knowing this helps you allocate marketing dollars appropriately ... your attribution models aren't going to tell you this, so it is a pretty important fact to know, right?

Give the Forensics Forecast methodology a try ... you're going to be fascinated by what you learn about how customers interact with the channels you support!

Mobile Analysis Week: Forecast Forensics By Channel

Over the previous four days, we analyzed a business that has a burgeoning mobile presence.  We know that mobile is something that resonates with customers of this business.

• Annual sales pass the eyeball test.
• Existing customers are more likely than average to use mobile.
• Mobile customers are becoming more likely to stay within the mobile channel than in the past.
• Online customers are becoming more likely to migrate to the mobile channel in the future.

As you already know, the majority of my consulting projects involve a complex five-year forecasting process ... a 16mb spreadsheet that predicts how customers will migrate across channels in the next five years.


Let's apply the spreadsheet to our business.  Click on the image below to make it larger:

In this forecast, I maintained the trends observed in the past year.  Each subsequent year, I decrease new online customers at the rate that they were decreasing due to increases in the mobile channel.  I increase new mobile and social customers at the same rates that they were increasing in the past two years.  And I use the same migration trends that we observed last year ... we know this is a false assumption, because customers are migrating to mobile at ever-increasing rates, but let's go with this assumption for the purposes of our blog post.

First of all, this business is not growing, it's a static business that is in balance between sales from new customers and sales lost by previously loyal customers.

Second, given the trends observed last year, we can clearly see that old-school channels like the telephone are in decline.  E-mail and Search are exhibiting modest growth.  Social is growing, but not at a rate that would impress even the most ardent supporter of the channel.

Mobile, however, is growing at a healthy rate, 21.1% per year, every year ... and this only takes into account what is likely to happen if trends stay the same as they were in 2010.  We know that this is unlikely.


If trends accelerate, we'll see mobile at $5 million or $8 million ... and we'll see the online channel suffer more as a consequence.  And when that happens, oh, look out ... organizationally, that kind of channel shift sends shock waves through career paths, investment strategies, and the "informal internal pecking order", if you will.

Again, let's look at the evidence:

• Annual sales pass the eyeball test.
• Existing customers are more likely than average to use mobile.
• Mobile customers are becoming more likely to stay within the mobile channel than in the past.
• Online customers are becoming more likely to migrate to the mobile channel in the future.
• Our five-year sales forecast, quite conservative in nature because it doesn't factor in changes in adoption rate, strongly suggests that e-commerce will suffer in the future as the mobile channel encourages channel shift.

This is the introductory analysis I advocate for all retail/e-commerce/catalog brands with a mobile or social presence ... it's step one of the process of learning the impact of a new channel on a legacy business.  If you can demonstrate each piece of evidence that we demonstrated, then as Executives, we have no choice but to dive-in with both feet, maximizing the mobile experience for our customers.


If, however, the data shows that mobile growth is more similar to the growth observed in the social channel in our analysis, well, then we don't have to run around with our hair on fire trying to maximize the mobile channel.


Make sense?


Ready to run your own analysis?


Need help?  Hire me, and I'll do it for you, I've got the tools to do it.  Click here to contact me.

Forecast Forensics: The Basics

Remember, you want to pay attention to Forecast Forensics if you are responsible for forecasting sales levels in 2011, 2012, 2013, 2014, and 2015 at your company.

Here's the basics ... you start with a population of customers, and this population of customers has a likelihood of buying something in the next year.  Take a look at the handwritten image below for details (click on it to make it bigger):

In our example, we start with 1,000 customers.  400 customers choose to buy again (40% retention or "rebuy" rate).

• 50 spend an average of $800.
• 175 spend an average of $600.
• 100 spend an average of $400.
• 40 spend an average of $200.
• 35 spend an average of $100.
• Total Spend = $196,500.

This is the process we go through in a Forecast Forensics project.  We calculate the probability of a customer purchasing again in the next year (or month if you're a subscription-based business).  If the customer purchases, we calculate the probability of the customer being a high-value customer (grade = A), or a low-value customer (grade = F).  The value of the customer determines the likelihood of the customer buying again in the next year.

Once we migrate customers into next year's grade (50 = A, 175 = B, 100 = C, 40 = D, 35 = F), we replicate the process ... we add new customers into the mix, we estimate whether lapsed customers buy again, we apply spending amounts to each customer grade, and we're set!

Now, grades can also mean "channel preference" ... we can identify the channels that customers prefer ... we'll get more interesting outcomes looking at the world this way ... we can see how, for instance, mobile influences the future of our business.

We iterate through this process for each of the next five years, yielding a sales forecast.

In upcoming editions of "Forecast Forensics", we'll walk through a series of examples, so that you can understand how to forecast the future trajectory of your business.

And if you're a CEO/CFO/VP-Marketing, looking to understand how your business is likely to evolve in the future, then take advantage of an introductory price for the first three clients.  Or if you're an owner looking to sell your business, sign up now:

• Project Fee = $3,200 for existing clients.
• Project Fee = $3,900 for new clients.
• Contact Me For Details --- Click Here.

Consulting Projects: Clarification

There's been a few questions in recent days about what you get when you sign up for various consulting projects.  Hopefully, this will clarify things for those of you looking to hire me this Spring.


Catalog Marketing PhD

• Target Audience:  Catalog CEOs, Catalog Marketing VPs.
• Average Cost:  Logarithmic Curve, $7,500 for small businesses, $75,000 for Wal-Mart.
• Average Gain:  A $50,000,000 business (housefile) will see $300,000 to $500,000 of annual profit, on average.  A few cases push to $1,000,000 or more, contact me for references.  In other words, it is typical to generate 20 times as much profit, per year, as you pay for the cost of the project.  That's a sweet deal, folks!
• What You Get:  Rolling twelve-month analysis of growth by channel, Multi-Channel Forensics analysis of each physical/ad channel so that you understand how all of your channels fit together, Digital Profile segmentation analysis, prediction of twelve-month profit by customer, calculation of "organic percentage", the amount of demand not generated by catalogs, prediction of twelve-month profit generated by catalogs, Forecast Forensics that tell you what you must do to grow your business in the next five years, and a customized catalog contact plan for each individual customer for the next year --- telling you exactly how many catalogs to mail each customer to generate optimal company profit.

Digital Profiles

• Target Audience:  E-Commerce CEOs & EVPs, Retail CEOs & EVPs
• Average Cost:  Logarithmic Curve, $5,000 for small businesses, $40,000 for Wal-Mart.
• Average Gain:  Determined by how the client chooses to use the results.
• What You Get:  Digital Profile segmentation analysis, yielding sixteen or more actionable segments that comprise channel preference (e-commerce, e-mail, search, affiliates, social, mobile), merchandise preference, geographic location, lifestage/psychographic/demographic information, credit information.  Digital Profiles are most often used to analyze e-mail campaign performance and to target customers for specific e-mail versions ... project results are also used to analyze online item performance by Digital Profile.  In addition, client gets Forecast Forensics, which tell the CEO/EVP what must be done to grow your business in the next five years, by channel.

Forecast Forensics

• Target Audience:  Owners, CEOs, EVPs, VP of Marketing
  
• Average Cost: For the first three clients, $3,200 for existing clients, $3,900 for new clients.  Prices are likely to increase after the first three clients are accepted.
• Average Gain:  Determined by how the client chooses to use the results.
• What You Get:  A spreadsheet that predicts where your business is headed, given various advertising strategies by channel, various retention strategies, and various new customer acquisition strategies.  You will obtain predictions, by channel, for total sales over the next five years.  You will be able to simulate different strategies (i.e. doubling your e-mail frequency, increasing your search budget, ramping-up your mobile strategy), and you'll be able to see where your business heads as a result.  I anticipate this will be a popular project among E-Commerce Executives struggling to understand the impact of mobile/social on their business.  I anticipate this will be a popular project among Catalog Owners looking to sell their business in the next year.

Please e-mail me with any questions you have, I'll be happy to answer them for you.

Forecast Forensics: A New Topic/Product For CEOs, CFOs, EVPs, and VPs

We're going to begin a new topic here on The MineThatData Blog.

Starting next Thursday, we will spend one day a week talking about "forecasting the future".  I am calling the series "Forecast Forensics".


Over the next several weeks, I'll explain what "Forecast Forensics" are all about.  Many of my clients already enjoy a version of Forecast Forensics as part of their Catalog PhD project or Multichannel Forensics project.

In this case, the entire project is designed to let the CEO, CFO, EVP, or VP of Marketing predict the future.  We analyze a series of offline purchases, online purchases, mobile purchases, and purchases attributed to social media.  The data is structured in a manner that allows the business leader to understand where the business is headed, over the next five years (click the image below to make it more readable).

 

The spreadsheet application allows you to answer a myriad of questions:

• What will my sales look like in five years?
• Will emerging channels (mobile) cause the cannibalization of existing channels (e-commerce)?
• What impact will search have on my business over the next several years?
• What happens when I double my e-mail contact frequency?
• What happens when I eliminate my e-mail marketing program?
• Does social media truly contribute to the bottom line?
• How will my customer base evolve and change over time?
• How many new customers do I need in order to allow my business to grow?
• What has to happen to my retention rate in order to get my business into high-growth mode?
• What happens to my business if the economy plunges by 5% this year?
• What is the growth potential of my business, given that I'm thinking of selling my company?

The price is right if you are an e-commerce executive, retail business leader, an owner of a catalog business, or a small business owner.

• First three clients to sign-up:  $3,200 for existing clients, $3,900 for new clients.
• Prices are likely to increase once three clients sign up.

Over the next few months, we'll talk about Forecast Forensics each Thursday, so that you can see what the methodology is all about ... we'll explore the questions outlined above in greater detail.  In the mean time, if you're one of the first three to sign up for a project, you get a really good deal on a valuable forecast tool!