The optimism of #Purpose & science of #design in forging #digital customer experiences! Opinions my own!

” Not everything that can be counted counts, and not everything that counts can be counted.”

– Albert Einstein

Don’t pat yourself too many times on the back – not all sales can or should be attributed to what we (marketing) do. There’s always the sales vs. marketing disconnect, so where does it start, where does it end?

Marketing, according to Sales, clogs the funnel with useless leads. Sales, in our eyes, they ignore all the leads we’ve lovingly prepared for them, all the “silver bullets” for conversion.

The fact is, if we turned off all marketing today, the company would still see some portion of sales coming in. If we have a (bank) physical branch, a portion of sales may be attributed to marketing, but we shouldn’t receive credit for the branch walk-in that happened to open an account with us while waiting for an appointment across the street.

The more established your brand, the bigger role brand equity plays cf. a newer brand with a smaller loyal customer base. All of the millions of dollars that were spent over many years of marketing play into that brand loyalty – else Wannaker was right and 50% of our marketing dollars was indeed wasted. If a customer has a habit of visiting our branch every Wednesday after lunch for 12 weeks in a row, and again visits in week 13 – should the email, call-out, mailer etc that we happened to send that morning receive the credit, or should some or most of that credit go to the fact that we’re already customer driven?

The point of revenue attribution is to know what is working and to understand what levers to adjust to optimize marketing spend. Without giving appropriate credit to customer driven sales, too much credit would go to marketing execution – which may be rarely the case in your organization – but this would lead to overspending in an area that may not drive the anticipated incremental sales.

By definition, revenue attribution is retrospective. All of the sales have come in and the marketing budget has been spent. Attribution is simply dividing up the revenue to the appropriate channel. Without a proper test and learn strategy, attribution may be contentious. Agree on a test, upfront. Define what success looks like with all relevant parties – Sales and Distribution especially.

Answer these 3 questions first:

1. What impact will the marketing program have on key performance indicators if executed across the network or customer base?
2. Will the program have a larger impact on some branches/customers than others?
3. Which components of the idea are actually working?

Consequences

If marketers don’t attribute sales and leads to the appropriate channels, their understanding of various expenditures’ contributions will be skewed and will result in marketing decisions that don’t produce desired outcomes. Nevertheless, many marketers still rely on a “last touch” approach for assigning credit to their marketing activities.

Fractional or partial attribution allocation methods are a step in the right direction. But these tend to be judgment based and produce questionable accuracy. Furthermore, most marketers are overwhelmed by how to get started.

Fortunately, there is a new approach, or framework, that addresses multichannel attribution. This framework leverages promotional data in combination with modeling techniques and universal control groups. Using this approach allows marketers to better discern, at an individual level, each channel’s influence on demand generation.

Components of this alternative framework include:

1. A multi-channel marketing test and control design establishing baselines for contribution by channel. For example, direct mail might produce three times the demand of email; email might produce two times the demand of banner ads and so on.

Marketers determine these by holding out channels to various control groups to measure the incremental impact on sales. The metrics subsequently form the basis for channel weights in the attribution algorithm.

2. Construction of channel-based incremental response models, which are used to calibrate channel weights at an individual level. This helps better gauge channel contribution based on each customer or prospect’s specific channel propensities.

Consider Magic Mike, a hypothetical Bank customer who has been determined by his past behavior to be highly responsive to email – say 3.2 times as likely as an average customer to respond to this channel. If the average customer’s baseline for email response is 100, Mike’s uplift model index for email would be 320 (3.2 x 100).

Now, determined have determined email has a channel weight of 0.65 – that is, email is only about two thirds as productive as all the direct channels in the marketing program. Mike’s individualised email channel weight is 1.43 (0.65 X 3.2).

In a similar fashion, the marketer can calculate individualized direct mail channel weights. For Magic Mike, the individualized direct mail channel weight is 4.29, or three times that of email. Therefore, when Magic Mike makes a purchase, direct mail gets three times as much credit for the purchase as email. For every $100 Magic Mike spends, $75 would be attributed to direct mail, and $25 would be attributed to email, assuming Mike was exposed to both direct and email efforts.

3. The development of response curves for each channel, such as direct mail, email and banner advertisements. Response curves allow marketers to adjust the weight given to channels based on the last time a customer or prospect was touched by that channel, relative to the purchase date. The closer the marketing touch to the purchase, the greater the contribution. Don’t forget actual contribution figures as well – it’s one thing to solicit responses, another to close deals and sign contracts. For the former, perhaps an email or a virtual telesales team to setup meetings. For the latter, most definitely a branch visit or face to face meeting with a relationship manager.

Response curves can also be used to determine the appropriate attribution windows to use in the attribution algorithm. The data may show, for example, that email contributes to purchase behavior for up to two weeks, while direct mail contributes up to a month.

4. Universal control group design and implementation, which is used to account for the effects of mass media and other demand generators. This ensures that direct marketing channels are not over-credited in the attribution algorithm; which affects product development cycles as well, e.g. if we plan on bundling core products/services to move the PPC needle.