chocolate bar with REaD Group logo and title 'The propensity to eat chocolate'

By Scott Logie, MD, Insight at REaD Group

Propensity. It’s a funny word, and one we throw around a lot in marketing circles but rarely in everyday use.  The propensity to donate to a certain charity, the propensity for a customer to lapse, the propensity to make a repeat purchase. We can even build propensity models to try and predict these outcomes.

However, you wouldn’t ordinarily hear someone say that they have a propensity to eat chocolate late at night (as I do). Or perhaps that their dog has a propensity to wolf down her food (you can be sure mine does) or even that their wife has a propensity to buy more shoes than could be worn in a lifetime (…no comment).

What is propensity?

So why is the term used so frequently in marketing?  I suppose it satisfies a need we have for wanting to know what our customers are likely to do next. When we talk about propensity, what we really mean is the inclination for someone to do one thing more than any other.

For the purposes of marketing we’re looking for people who are more likely to do the thing we want – or don’t want – them to do.

As with most things in life, this comes down to probability and the likelihood of something happening. So, let’s say that when I stay in London, I buy chocolate two nights out of three when I’m heading back to my hotel. This would make the likelihood of me doing this the next time I’m in London two thirds. It may be that I don’t fancy chocolate so much when the weather is hot, and this likelihood can be increased or decreased accordingly depending on the weather. There may be any number of variables that will affect the probability (but more often that not, I’m going home with a chocolate bar).

But can my propensity to buy late night chocolate help to predict if other people will?

Well yes – if you know they’re Scottish (and are therefore always craving sugar) or that they can never say no to a Snickers after having a few pints…or are convinced that going for a run the next morning justifies the eating of said Snickers. With the right data to understand the driving factors behind the decision, we can predict the likelihood of others doing so.

Individual vs Group

This can be approached in two ways. First of all, at an individual level – what is the probability that I buy some chocolate tonight? And then secondly – in a certain group, who is most likely to buy chocolate later tonight? Having this insight will help to sell more chocolate (or to combat diabetes if the data is used more responsibly).

This doesn’t just apply to chocolate. The same principle applies when selling a product, asking for donations, encouraging someone to renew their car insurance or book an all-inclusive holiday. The trick is finding the people who have a propensity to do these things more than others.

On an individual basis we can use past behaviour (of a person and of others) to work out how each person is likely to interact with a company next – often called next best action. This involves looking at past behaviour, current status (their last purchase – when this was and how much they paid) comparing their behaviour to lookalike customers – and also considering what we would like to sell!

Super models

By taking all of these things into consideration we can construct a model for each person that scores all the probable decisions and chooses the ones that are most likely to happen. This can also be weighted accordingly to areas that are most profitable.

There is a slightly different outcome at group level, but it is a very similar approach. A score is applied to everyone and we then select those with the highest propensity to do that thing.

For instance, if we are organising an event we want to make sure we choose those who are most likely to take part in that event – whether that be high propensity to run an Iron Man, eat an excessive amount of chocolate or to bungee jump from a skyscraper.

Using the right data

Whichever approach is taken, it is essential to have enough data and the right data in order to build the models. The necessary data should exist inside your business – in the form of data you have on existing customers and past behaviour and outcomes (such as who lapsed and why). It can also be useful to incorporate external data – such as online activity, social media and general activity outside your business.

Ensuring that we are harnessing the right data and applying it to our customers and to the outcomes we are interested in will ultimately increase our propensity to make better decisions. Which is what we all want to do, surely? Perhaps I need to remember that the next time I get peckish after a few beers!

The teams at Marie Curie and REaD Group, winners at the Insight in Fundraising awards 2019

REaD Group are delighted to have won the Most Powerful Insight using Data Analysis award with our fantastic client, Marie Curie, at the Insight in Fundraising Awards last night! The awards recognise the best work, innovation and inspirational stories of individuals working in data, analysis and insight contributing to fundraising practices.

Marie Curie delivers nursing and care services across the UK, and people interact with the charity through many touchpoints ranging from hospices to shops. They were keen to understand what influenced this giving and how much of an impact local services, such as shops, hospices and nursing care impacts on the money received in donations and other giving.

A carefully constructed methodology enabled REaD Group to create a baseline fundraising landscape onto which Marie Curie-specific variables were overlaid.

Analysis of these ‘layers’ generated predictive models that were then mapped, highlighting areas where fundraising performance is above or below expected levels, in the context of the provision of services in the locality.

The models built have been used to show the impact each of the major activities have had on fundraising and other forms of giving.

For example, in a local community it is possible to quantify the impact of a hospice, a shop and the local fundraising group.

Understanding these impacts has not only provided invaluable insight for the charity to inform business and marketing decisions, it has also supported the business case for different areas of the organisation working more closely together.

A fantastic evening and so inspiring to see the wealth of talent and innovation being achieved in our industry. Huge congratulations to everyone involved!

hand holding four aces and two die

By Scott Logie, MD, Insight at REaD Group

I recently sat down to play a great new board game with my wife and two statistician friends (there was also lots of wine, food and great chat involved). The game is Borel: https://www.playborel.com. The basic concept of the game is fairly simple: through a series of experiments using dice, cards and coins, it seeks to find whether intuition beats statistical reasoning. Of course, there are conditions applied so that there isn’t really enough time to be too statistical, but it works as a concept.

As an example, if you were to roll three six-sided dice six times, will any consecutive numbers be rolled? Basic stats suggest this should be a no, but when we did this experiment the first two numbers rolled were one, and then we rolled again and got another one.  We were so flabbergasted at this that we re-ran the experiment and immediately rolled two fives.  A triumph for intuition it has to be said.

It’s probably no bad thing that we don’t get to play fast and loose with money in this way. In business, the most important thing is to ensure we have adequate data to make decisions and to increase the probability of those decisions being correct.

Retail’s gut feel

Imagine looking for a new store location, but not considering the road network, the parking availability, the demographics of those who shop in the area, their disposable income and the likelihood of them buying the products you sell. That would be unthinkable: except that not so long ago, siting locations for stores was done very much on instinct. It’s only been in recent years that all these factors have come to play a part, thus ensuring that there is every probability that a new store will be in the most successful location possible.

Recently I watched an interview with one of Sports Direct’s directors. He said that their decision over which failing store groups they bid to take over was based on gut feel. While retail has a notoriously strong reputation for gut feel, I’m also pretty certain they have a formula: a way to evaluate the potential in a business to remove as much risk as possible and optimise the likelihood of success.

Note that many of these phrases are statistical by nature: every probability, remove risk, optimise success, increase the likelihood. We use stats every day, without thinking about it.  Can you guarantee success?, we are often asked.  No, but we can increase the probability of it happening.

Probability in marketing

As marketers, we don’t make huge decisions about investments in new stores or which companies to take over on a daily basis. But we do get measured on the success or otherwise of our campaigns, and of course we try to ensure that we weight the odds in our favour as best we can. Every day we use probability to ensure that we are delivering the right message to the right person at the right time.

Outbound communications, for example, are all about maximising returns: contacting the most relevant individuals with the minimum investment. To do this we use profiles, models and segmentations to help us understand as much as we can about our targets, remove those least likely to respond and find those who are more likely to want to buy our products.

Online, there are different ways to find the most relevant targets. Sometimes it is left to machines to help us do this, but in the background are similar algorithms, finding people (or cookies of people) who look like they browsed the same sites as those who clicked through.  Even the way we find these cookies uses statistics, using probabilistic matching to try and find the same person across numerous machines.

Making your marketing as good as it can be

As with all modelling, the more data we have, the more observations of an event, the more variables we can vary, then the better our decisions will be. Our mantra at REaD Group is that the more you know about an individual the better your marketing will be, which is why we are always looking for data to help us create a more complete picture of our customers’ customers. Sometimes that data is at the individual level and sometimes at the household or even the postcode they live in. In the end, though, it is all about probability and having a better chance of getting a response to a campaign.

One of the joys of Borel was that the number of observations was kept small, the number of variables was low and the time to make a decision was short. Hopefully by adding more data, building up more history and ensuring that more information is available, we can help our clients make better decisions by providing more information.

Incidentally, I won the game by the slimmest possible margin and my wife, who based everything on informed hunches, was right behind me. I’d love to think my stats background gave me the edge. But maybe we need to play again just to be sure.

This blog post originally appeared on Decision Marketing: https://www.decisionmarketing.co.uk/views/data-driven-decisions-are-better-than-a-hunch-right

 

CIM Marketing Excellence Award Finalists - REaD Group

REaD Group are delighted to have been announced as a finalist for the CIM Marketing Excellence Awards 2019! In partnership with Ageas Direct, we have been shortlisted in the Best Use of Data and Insight category. The award recognises the intelligent use of data to create compelling insights, resulting in an improved product or customer experience.

The CIM Marketing Excellence Awards identify and celebrate outstanding marketing by organisations, individuals and teams. Now in their tenth year, these awards continue to recognise that high standards of quality and integrity are vital to the success of marketing, as well as rewarding the innovation delivered by marketers who are at the cutting-edge of their profession.

Using data and insight intelligently to better understand your customers should be at the forefront of every marketer’s mind.  With the wealth of data available it makes perfect sense to utilise such an invaluable asset to improve personalisation and customer experience (and drive competitive advantage!) – provided that this data is used responsibly.

We’re looking forward to awards night on 11th April – see you there!

Get in touch for more information on our engagement services

cartoon of REaD Group Director, Scott Logie, running with a glass of wine surrounded by a dog, 3 cats and chickens as an example of segmentation

by Scott Logie, MD, Insight at REaD Group

Many years ago, in the last millennium in fact, I worked in a large UK Bank.  One of the projects we undertook was to segment our customer base.  We started by breaking it down into lifestages, clustered each lifestage and then grouped them together.  We then overlaid a lot of data including attitudes, lifestyle and detailed customer research by segment.  This segmentation was then responsible for helping create the underlying marketing strategy.  And one that worked amazingly well, we were getting up to 25% response rate on some of our outbound direct mail campaigns.

This wasn’t my first segmentation project although it was probably the biggest I’d tackled at that point.  Since then I have been involved in many many more and frankly, I love them. Not just from a data point of view – they are pretty fun though – but also because they always throw up some exciting, interesting and useful segments for our clients.

Over the years, I’ve got pretty pissed off hearing about the death of segmentation.  Because we can track the behaviour of every individual on-line, and have the technology to create bespoke plans for each of them, there is now no need to segment.

The truth is that when you have millions of customers, and prospects, to engage with, you can’t make every decision based on a detailed and personalised plan for every individual. So segmentation is actually still really useful and for me is the bridge between mass marketing and the nirvana of one-to-one marketing.

And segmentation exists in endless varieties. From the micro-segmentation of traffic arriving at websites, to the attitudinal or behavioural segmentation of a brand’s customers, to the socio-economic segmentation of voters, to the geo-demographic segmentation of media consumers, there is segmentation at work everywhere, and with increasing sophistication.

Segmentation is quite straight forward to do and powerful when used correctly. While each of us are our own person, in many ways we still act like a lot of other people. We actually do exhibit common patterns of behaviours and attitudes, and it is useful for brands to acknowledge and act on those patterns.

However, there are some important considerations when looking to create a segmentation.

First, be clear about the usage that a given segmentation approach is intended to address. All segmentations answer some questions but no segmentation answers all questions. Maybe you want to retain your most valuable segments then the segmentation needs to be lead by value.  Maybe you want to understand where there is market potential, then the segmentation needs to address an overall market.  Or maybe you need to understand the demographics and behaviours of your customers to drive content and creative, then the segmentation needs to be demographics led.  It might sound obvious but a lot of segmentations are done without thinking about how they will be used.

Secondly, think about the data.  A lot of the time I feel that data is chucked at a segmentation.  I’ve been guilty of this myself, just throw all the data in and see what happens.  Over time, I’ve learned that this is dangerous.  Notwithstanding all the statto needs to normalise, scale and deal with data anomalies there are other important things to consider.  The most important of these I believe is to split the data into what is going to be useful to create the segments and what is better being used to describe the segments – this is not always the same data.    

Finally, this is not a data project.  Segmentation is a customer project.  I know it starts with the data but it should end with creative ways to engage customers and prospects and, sadly, that is never going to happen if the project sits in a data team (sorry geeks).  So it is really important to engage the whole team early, get them to understand what is being done and why and that this project will fly if they get involved and give it some life.  Some of the best projects I’ve been involved in are the ones where creative marketers owned the segmentation.

So the next time someone tells you that segmentation is dead, tell them you don’t think so.  In fact, not only is it not dead but it is alive and well and thriving for brands that want to build bespoke campaigns for their customers.  Tell them you are proud to be one of the people who sits in the segment called “believers”.