Hillstrom's Digital Profiles: An eBook

Sometimes, I get feedback that goes something little like this:

• "I'd like to apply Digital Profiles to my business, but I cannot afford your consulting services.  How can you help me?"

Today, I offer you a solution.

For $95.00, you can download a new eBook, called "Hillstrom's Digital Profiles".

• Download the free Kindle eBook reader here.
• Hillstrom's Digital Profiles:  Amazon Kindle, $95.00.
• Hillstrom's Digital Profiles:  B/N Nook, $95.00.

In the eBook, you'll get all of the tools necessary to create your own Digital Profiles.

• A 40mb dataset that you can practice creating Digital Profiles with.
• The SPSS code that creates Digital Profiles.
• My commentary on the programming and analysis process.

Yes, I am ready for your next question.

• "Are you insane?  I have to pay $95.00 for this?  This is 2011, stuff is free everywhere on the internet.  Why can't you give this to us for free, or for a reasonable cost like $0.99?"

If that's your question, then keep the following in mind:

• Every detail to create your own Digital Profiles, except for the SPSS code, has been published on this site.  Simply search for Digital Profiles, and you have all of the content you want for free.  Is that a bad thing?

• In the comments section, list all of the vendors who give away all of their trade secrets for free on a blog.  Whatever work I do for clients, I always publish details of the analysis, for free, on this blog.  I hope that is acceptable to you.

Get your corporate credit card out, and purchase the eBook.  Download the code, download the dataset, and create your own Digital Profiles.  Then apply what you've learned with your own data!

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!

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.

Hashtag Analytics: Free Spreadsheets And A Booklet!

You knew it was coming!  You wanted a concise methodology for forecasting the future of your social media community.  And now you have it.

Hillstrom's Hashtag Analytics is a soup-to-nuts methodology for forecasting the future of a social media community on Twitter.

What do you get?

• A FREE dataset (in .csv format) that contains eight weeks of participant behavior in the #blogchat community, summarized at a participant/week level.

• A FREE spreadsheet that allows you to forecast the future trajectory of your social media community (you will have to write the programming code to get your data into the spreadsheet).

• 44 pages of text that outline the thought process behind forecasting the future trajectory of a social media community on Twitter.

You are unlikely to find anything of this nature from social media analytics experts, and if you do find something that allows you to forecast the future of a social media community on Twitter, you're going to pay an agency a hundred thousand dollars for the right to do the forecasting!

This booklet is available in three formats.

• Available in print from Amazon.com for $7.95.
• Available via Amazon Kindle for $2.99.
• Available via Barnes & Noble Nook for $2.99.

If, after you buy the book, you find that you want an expert to run a forecast for your social media community on Twitter, give me a holler, I'll be happy to perform the analysis for you!

Hashtag Analytics: Part 10 = Four Month Forecast

We know the probability of a #blogchat participant engaging again in the next four weeks.

We know the Digital Profile the #blogchat participant will migrate to if the participant engages.

We know how many new participants we'll have in the next four weeks, by Digital Profile.

This allows us to create a simulation, illustrating how the community will evolve over time!

Well, we have good news here ... the community was at 2,193 monthly participants, and is forecast to increase to 2,403 participants, then 2,485 participants, then 2,518 participants, then 2,532 participants over the next four months.  Remember, growth isn't coming from engagement rates ... growth is instead coming from new participants!


We can also forecast where key metrics are headed.  We know that the #blogchat community will grow by 16%, what will happen to tweets and other key metrics?

Well, this is a positive story!


What's happening is that the participants who are engaged are moving into more valuable Digital Profiles, Digital Profiles where participants are more likely to tweet and participate at high levels!




What Did We Learn?


We learned that the #blogchat community is a vibrant and successful community.


We learned that engagement rates are generally low, and that is perfectly acceptable.


We learned that a small number of participants generate most of the "oxygen" for this community ... we called them "Mega Participants".

We learned that kindness matters!!!  We learned that the simple act of thanking a first-time participant who retweets content yields an engagement rate that is up to ten times greater than observed when a first time participant is not acknowledged for a retweet of content.

We learned about Digital Profiles, descriptions of various participants that have predictive ability.

We learned that there is a common path that a participant takes as the participant goes from a first tweet to "Making A Statement" and participating at a high level.


We learned that participant growth will come from new participants, and that is perfectly acceptable (and is congruent with most of the e-commerce, retail, and catalog work I do).

We learned that the #blogchat community is growing at a 16% rate over four months.

We learned that total tweets within the #blogchat community is growing at a 40% rate, because many participants are moving into high-value Digital Profiles!!!!

We learned that, overall, Mack Collier and his #blogchat community is thriving and succeeding, a good thing!!!

We learned that we can predict the future ... not many social media analytics experts have a methodology for predicting tweet volume and participant volume ... we, however, have a methodology for doing this!!!


What's Next?

Tomorrow, we conclude our series with the introduction of a booklet that teaches us how to predict the future of a social media community on Twitter!

Hashtag Analytics: Part 9 = Seeing The Future

The beautiful thing about Digital Profiles is that we can see where participants are likely to migrate to, given the Digital Profile they reside in.

Let's take the Digital Profile a participant belonged in last month, and measure the probability of a participant engaging again in the next month.

As we've talked about all throughout this series, engagement rates are not high, and that's not a bad thing ... the #blogchat community does a sensational job of recruiting new participants!

If a participant engages again, we can look at the Digital Profiles that the participant is likely to migrate to.  This table illustrates counts by Digital Profile:

There is a logical path that a participant navigates ... the participant usually migrates to "Joining The Conversation" and then to "Shaping The Conversation".  Sometimes, the user migrates to "Making A Statement" and then to "Shaping The Conversation".

Regardless, participation and migration paths make sense.

If we know what engagement rates are, if we know what migration patterns are, and if we know what new participant counts are, we can create a simulation that allows us to forecast the future trajectory of the #blogchat community.

We'll finish our series tomorrow with a four-month forecast for the #blogchat community.

Digital Profiles: Loyalty Dynamics

If customers can downgrade their loyalty, they can also upgrade their loyalty, right?

Let's do something fun.

The top four Digital Profiles are, in terms of customer value:

• Gold Mine
• Multi-Channel Mavens
• Shop Online, Buy In-Store
• Retail Fanatics.

We'll call these four Digital Profiles "Valuable".

We'll call the remaining twelve Digital Profiles "Marginal".

I ran a query against this dataset. I wanted to see how customers migrate between "valuable" and "marginal" Digital Profiles. Here are the results.

Last Year's "Valuable" Digital Profiles:

• 8,852 customers downgraded to "Marginal" Digital Profiles.
  
• 10,272 customers maintained "Valuable" Digital Profile status.
• 7,150 customers did not purchase in the next twelve months.

Last Year's "Marginal" Digital Profiles:

• 27,510 customers maintained "Marginal" Digital Profile status.
• 8,011 customers upgraded to "Valuable" Digital Profile status.
• 55,935 customers did not purchase in the next twelve months.

Clearly, the customer file is going through a dynamic transformation, as "Valuable" Digital Profile customers downgrade their status, while a small number of "Marginal" Digital Profile customers upgrade their status.

Another interesting query is to see "where customers come from". Let's look at this year's "Valuable" Digital Profiles, and see what their status was in 2009:

• 8,011 upgraded from "Marginal" Digital Profile status.
• 10,272 maintained their "Valuable" Digital Profile status.
• 7,062 did not purchase last year!

Again, the customer file is a dynamic ecosystem, with considerable customer turnover.

You won't read about this stuff in the marketing literature, folks. But it is important ... really important. Once you realize that your customer file is truly an evolving ecosystem, you approach it in a different way. For once, I'll side with the Multi-Channel Pundits ... there are times when you want to "diversify your portfolio", constantly recruiting customers in a profitable manner from many channels in order to protect the health and diversity of your customer file!