McKinsey has defined ‘Modern Marketing’ for us!

McKinsey’s March article ‘Modern Marketing: what it is, what it isn’t, and how to do it’ takes a bold look at all the enablers and capabilities required for marketing today.

Importantly they define the goal of modern marketing as: ‘to leverage data from all consumer interactions to creatively deliver as much relevant one to one marketing as possible’.

This is interesting, not least for some things it omits, such as developing brand awareness.

The article does however give a broad spectrum of their recommendations as listed in the table below.

As providers of a customer data platform technology and data science, we can’t help being excited by the number of areas where the capabilities of our software and analytical services are required by the modern marketer, as indicated by the blue arrows.

Central to their concept of a customer-centric mindset is the need for: ‘a centralised data platform with a unified view of customers, culled from every possible touchpoint; the continuous generation of insights from customer-journey analytics; the measurement of everything consumers see and engage with; and the hiring and development of talented people who know how to translate insights about customers into experiences that resonate with customers’.

In another section they suggest that it is important to ‘elevate consumer insights and analytics’, and that ‘no marketing activities should be executed without the backing of relevant insights and the ability to measure performance’.

There is a lot we found to be of interest and we strongly recommend reading the full article.


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Keep it clean!

This somewhat short title reflects the overall approach to data cleansing suggested by our partners the REaD group in their latest report.

In other words, it’s no longer just one trip to the bathroom a year, but rather daily hygiene backed up by a culture that won’t accept any excuses for dirty data.

We partner with the REaD Group for a number of data cleansing services including PAF formatting to get address structures right, and suppression of gone aways and deceaseds.

The amount of dirty data residing in customer databases that they remove never fails to surprise us, or our clients.

If you would like to know how much you have, we can arrange a free audit; this will tell you just how many address duplicates there are, the amount of badly formatted addresses, and the number that should be suppressed.

If you want their report, then please read it here REaD Group Report, and if you would like to arrange an audit then please email us and we will call you to discuss.


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Is our day of reckoning coming soon?

No, you will be relieved to learn that this newsletter is not about climate change, but rather about how we marketers buy our media and measure its performance. Not so serious, but nevertheless extremely important.

At a recent conference organised in London by the Institute of Fundraising we asked an audience of around 70 people, all of whom work for charities, whether any had developed a reliable view of how different aspects of their marketing spend impacted their donations. Not a single person present was able to say yes.

In the distant past, before the internet had been invented, and when mail was the only direct channel, it was a whole sight simpler; all you needed to do was to create control groups that you didn’t mail, and then measure how they performed compared to those that you did.

In today’s multi-channel world, it’s a problem of very much greater complexity.

Our concern is that, because of the number of different channels and influences that can precede a customer action, like making an order, marketeers may have to a large extent given up.

But to do nothing leaves us with a £26bn per annum unanswered question just for the UK alone.

Clearly there are in fact two very different questions to answer; first how to infer the effect of non-direct media like outdoor advertising and most of TV, and second how to measure the effect of those channels that are direct like Google PPC, direct mail or Facebook?

Most practitioners who want to measure non-direct channels use some kind of time series modelling, and this works reasonably well when we are just looking at summarised data, such as the overall sales value of an organisation in January.

But there are big limitations in that it’s relatively expensive to develop the models, they cannot get into the detail of campaign performance rather than looking at aggregated channels, and they rely on the advertiser varying the amount of spend each month in each channel.

When looking at the outcome from a time series model there is also always a large proportion of sales whose cause cannot be explained by the model, and this has to be assumed as being due to the influence of the brand.

However, where marketeers appear to have thrown in the towel unnecessarily, is in respect of measuring the effect of direct media, looking at online and offline channels in combination.

We don’t believe that any organisation selling goods or services to identified individuals, such as home shopping companies or travel or financial services to name but three, has to give up on measuring the impact of their direct channels.

But to do this measurement one needs to work back from each order, rather than forward from the spend in each channels, to unravel what is actually going on in the real world.

We approach this by looking at all the known interactions between an organisation and a customer in a 90-day window before an order is received.

We ignore all clicks, opens, opportunities to view etc. etc. indeed anything that cannot be directly related to an actual order event, and treat these just as noise.

What then comes to the surface is much more complex that any last click proponent would like to admit; we find ourselves looking at a unified view in which emails, PPC, social, natural search, mobile SMS, direct mail, OBTM and any other direct channel employed can each play their part.

This table is a real example of just five individual orders received by a home shopping company, and counts the times each different channel played a role in the 90 days before the order:

Customer type Catalogues received emails received Google PPC Direct entry Phone in Total
Existing 2 3 1 1 7
Existing 2 1 3
Existing 8 2 10
New 3 3
New 3 13 1 17

Even in this relatively simple example the first thing that become apparent is the wide diversity of the routes taken by customers before they actually placed their order.

The good news though is that once you have joined the online and offline data together, and considered the weighting to give to different channels, and to different time intervals prior to an order being received, you have here the solid building blocks for attributing actual value to the channels being deployed.

You can then allocate the value of each order across the channels that influenced it, and end up with an overall value contributed by each channel in a particular time period.

We are not suggesting that this is rocket science, but it does need attention, and technology, to make it happen.

We have built the technology to automate this kind of attribution, and would be interested to discuss it with you if you felt it could help.

Please CLICK here for a short PowerPoint explaining in a little more detail how we do it.

 


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Dumping Cost Per Click

Have you ever felt that you wanted to dump cost per click (CPC) as your measure of ROI but time pressures and lack of tools mean that you remain stuck with it?

We have just received an interesting report from LinkedIn Marketing Solutions which explains how a large proportion of digital marketeers are still using CPC as their ROI measure for digital marketing.

But we all know that CPC, useful as it is, only describes one part of the customer journey.

Part of the problem is that, as the report explains, marketeers are under constant pressure to make decisions quickly; they have frequent budget allocation discussions and need to base decisions on something.

However, we suspect that a bigger issue is that digital marketeers don’t have the tools to measure the true return, based on their overall contribution to sales achieved, from their different online and offline media.

There will often be multiple influences on the journey to a sale, and many of these can be offline, like things sent through the mail, or undetectable on your website, like opened but unclicked emails. It is only when you look at all the online and offline influences in combination that you can start to allocate the value of a sale back to its causes.

This capability is precisely what we have developed in UniFida, so that we can bring together everything that may have influenced each of your customers in the 90-day window before they placed an order.

When you can see for each order both its value, and all the events that led up to it, and then weight them according to how recent they were, you can then do the value attribution job properly.

If you would like to find out more about how we can help you solve this problem, do please send us an email, and we will arrange a call at a time convenient to you.

Read the full report ‘The Long and the Short of ROI’ from LinkedIn Marketing Solutions.


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Solving the challenge of allocating recruitment budgets

Targeting Longer Term Customer Value

Using data science to target multi-channel marketing recruitment spend towards longer term customer value

This post will explain why targeting your marketing spend towards longer term customer value is not a one-dimensional problem. For instance, a group that may look bad by one criteria like retention may within it have sub groups that are of high value, and others that are the reverse.

Background

This case study has been derived from working with a substantial life insurance broker who has been using multiple channels to recruit customers in the UK.

We were asked to look at how best to target customer value as judged by the commission returned in the first 24 months from recruitment.

To give some insight into the kinds of issues we were presented with, lapsing early (i.e. in the first 24 months since inception) was one of the main causes of loss of customer value.

Lapsing early was found more amongst people recruited via outbound direct marketing channels than through more self-driven channels such as inbound to web. It was also correlated with the sum assured and the monthly premium. However direct recruiting is cheaper and higher premiums bring more commission.

The relationship between lapsing early and age was non-linear, with both younger and older people lapsing more than those in the middle.

As well as different recruitment channels having different costs per acquisition they also attract different age groups and sums assured.

Approach

So with so many factors at work how did we set about finding the best marketing tactics to deliver the highest return on recruitment marketing spend?

  1. We discovered that lapses in the first six months are very reliable predictors of the propensity to lapse within 24 months for every customer group (we got an R² of 0.99 when fitting our predicted curve to the observed data); this meant that we did not have to wait for 24months worth of history before making a prediction of lapse rates.
  2. The customer groups we were using were defined by a combination of age range, sum assured, and channel.
  3. We used a combination of known lapse experience with predicted to build the overall expectation not only of overall lapse rates but also when lapses would occur.
  4. The net customer value metric we used was the commission income up to 24 months after probability of lapse minus the cost of recruitment from the channel used.
  5. Hence for each customer group we could predict the net customer value; we also knew the historic number of policies being sold in each group and hence we could calculate their overall value.
  6. The net customer value by customer group ranged from -£1X to +£5X. This enabled our client to focus their marketing on areas where the net contribution was positive, customer numbers were substantial, and the required age groups could be targeted.

In conclusion

The focus for this client’s marketing turned away from broad brush channels like daytime TV towards direct marketing approaches. This switch made a very substantial difference to returns but with two caveats. First many direct marketing channels have a finite capacity, and second all recruitment channels suffer from saturation; in other words, the greater your spend in them, the worse the return.

However we strongly recommend that when looking at the effectiveness of different marketing tactics that you take a multivariate approach; that is allow the full effect of all factors on net customer value to be taken into account.


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Multi-channel Marketing Mix Optimisation

How to optimise the spread of your marketing budget


The Situation

A substantial insurer selling directly to consumers uses budget allocation software to optimise recruitment ROI across channels

The Client’s Business Goals

  • To use historic campaign performance metrics combined with a channel mix performance model to inform budget allocation
  • To trial our budget allocation software called BAT to optimise the way that money is allocated over 300+ activities each year
  • To use the redistribution of budget recommended by BAT to challenge the current allocation of marketing budget
  • To generate as a result an uplift of >5% of value from the same amount of overall marketing recruitment spend

Our Solution

  • We set up a joint team with the client to analyse historic marketing campaign metrics stretching back up to three years
  • From these we developed some 20 different channel level saturation curves showing how ROI declines in any channel as spend is increased
  • The historic metrics, combined with channel saturation curves, were loaded into BAT, along with the client’s multi-channel mix performance model to handle halo effects and re-attribution of some of the web demand
  • We used BAT to run optimised budget distribution scenarios, introducing cut-offs at an activity level in terms of minimum and maximum permissible spends
  • We then undertook with the client a budget planning process review to fathom out how best to introduce BAT and its outputs into the current planning cycle
  • Finally we trained the client to use BAT so that they became confident to drive it on their own

Key benefits

  • The overall business benefit was to get a substantial uplift in the value of sales from the same budget. We are not allowed to quote the uplift obtained but it gave an ROI on the cost of our software and services well in excess of x 20.
  • A second major benefit was the improved speed to develop budget plans; with over 300 campaigns pa and multiple channels in play, budget planning was taking many days to complete. This has now been reduced to seconds once the parameters for a new scenarios have been set
  • Lastly BAT has provided planners with the benefit of a full audit trail; all input metrics, saturation curves used and assumptions made are documented for each scenario. Additionally the BAT tool now holds a saturation curve library for use in future planning.

UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


What can possibly go wrong when I allocate my marketing budget according to campaign ROI?

Well, depending on exactly how you do that, quite a lot actually!

Let me give three examples:

1. If you approve any campaign expected to provide an ROI > X you may be approving campaigns for which there may be alternatives that yield better X+++ returns. These may be different types of campaign with the same objective, or for instance the same campaign but just run at a different time of year.

2. It’s well known that all channels can suffer from over-use or saturation, at which point their ROI will start to drop. Just think of the irritation caused by a TV advert repeating too often. But if you cannot tell what the cumulative ROI of all your campaigns in a channel is, how are you going to be able to measure this effect, and know when to reduce spend in one channel or increase it in another?

3. Campaigns don’t just happen in isolation; they happen within the context of all the other campaigns that are happening at the same time, each of which may have an impact on a potential customer. Hence you spend on DRTV will impact your returns from direct mail or door-drops. Understanding the impact of your main channels on other channels can become critical.

So how to resolve these factors, and get to the point where your marketing budget allocation takes account of them?

We have developed a process, and related technology, that is designed to help you get to this point. The way it works in detail varies with each client, because no two clients share the same marketing campaign mix, but the outcome will be that you have a marketing budget that should get you the best possible return.

Contact us for more information and a no-obligation chat.


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


What would you give to know how to stop recruiting unprofitable customers?

In many of our engagements with clients we discover that a high proportion of customers recruited are unprofitable, and for every one of these the company suffers not only their wasted sales and marketing expense, but also a downstream negative return.

For instance, life insurers may be concerned about claims risk, but often a greater cause of lost revenue is customer attrition. If they can recruit customers who stick, and pay higher premiums, this can transform their longer term profitability.

So why not move from a focus on recruitment volume, to one of recruitment value? It may mean lower customer volumes, but it will ensure much better overall profitability.

We want to share with you our five-step approach for stopping recruitment spend going on unprofitable customers;

1. Identify the factors that drive individual customer profitability.
These are going to vary from industry to industry, but they often boil down to purchase value, product margin, and product tenure. In simple terms if the customer buys from the bottom of your range an item that is expensive to deliver, and never repurchases, they are not going to be a prime recruitment target.

2. Analyse the recruitment process features that have an impact on these profitability drivers.
For instance different channels, and tactics within the channel, are going to drive very different types of customer. For one client we compared the longevity of customers recruited through their TV advertising and website (inbound traffic) with those recruited through outbound activities like telemarketing. The inbound traffic was very much stickier once recruited. But also, the types of customers you recruit will have a very strong impact; we like to look at differences in customer contribution by factors such as age, affluence, gender, geography etc.

3. Combining recruitment process features into a contribution calculation.
Once we understand those aspects of your recruitment process that are both controllable, and which have a significant impact on downstream customer contribution, we can then look at how in combination they can be used to identify and predict the future value of recruits.
For a recent client, we found that we had around 1000 combinations of factors that we could identify, that each had a significant number of recruits, and for which we could compute the average longer-term contribution. For instance a combination of factors could be age-band combined with recruitment channel, and particular product features.
For each combination, we could then analyse the average customer contribution provided. This provides the lens through which we can predict longer term customer contribution across all current recruitment activities.

4. Building a heat-map of where to spend the recruitment budget
By combining these individual customer segments into higher level groupings, we can depict go and no-go areas for targeting the recruitment budget.
This approach to targeting needs to be run in combination with an analysis of the cost to recruit by the different recruitment process features. The ideal is to find combinations of features with a low cost to recruit and a high customer value, but this may often yield very few available options. A pragmatic approach is to look at the ratio between downstream customer value and recruitment cost. Using the lens of recruitment process features this becomes very achievable.

5. Predicting forward value from your more recent recruitment activities
A final use of this analytical approach is to model the predicted downstream value of customer recruits in a particular time-period. To do this we allocate recruits in the period by their recruitment process features to a predicted contribution group.
The good news about this last step is that one can validate it on historic data.

We usually manage to get to a high level of accuracy of prediction of the drivers of customer contribution; our latest example was better than plus or minus 2%!