Should you combine several technologies to better orchestrate customer experience?

conductor orchestrating

Customer experience (CX) has become one of those hackneyed phrases that can mean different things to different people, but nevertheless getting it right is always going to be a key task for any marketer.

Customer experience management is changing rapidly with global changes such as privacy regulations and changes to the use of third-party cookies. As such, there isn’t one technology supplier who can orchestrate the end-to-end experience or solve your business problems in one platform.

Perhaps as a result of the varied interpretations of what customer experience means, different approaches and technologies have developed that vie for the attention of marketers wanting to get customer engagement right.

In our opinion many of these tools are perhaps better viewed as complementary to each other, rather than competing, although there is often a level of overlap between them.

If one looks at what are the underlying factors that drive engagement, a key measure for customer experience, there are at least four key dimensions, which can be portrayed like this:

factors driving customer engagement
Factors driving engagement

 

So how important is it to focus on customer engagement?

We have recently received the results of live marketing experiments from AKI technologies where they undertook a number of tests using different creatives for different segments of their audience. The results they got for increased engagement using personalised content based on segments was an uplift of between +33% and +280%.

Their insights also demonstrated that personalised content was more important at the start and the end of the customer journey than in the middle.

Cart abandonment is worth $4 trillion globally, so with an average of 70% of carts being abandoned, timing is a key contributor to customer experience. There are multiple claims for increased sales using event-based trigger marketing such as dropped baskets. Some companies such as Fresh Relevance claim a 10% improvement in overall sales from introducing dropped basket follow ups.

Other companies use relevance; for instance, ShopBox.ai expect to increase overall sales conversion from a website by +5% by using AI to propose alternatives to items that customers are currently looking at on a website.

Clearly improving the customer experience is getting results, although the exact metrics are going to vary considerably from one environment to another. So how should companies approach improving this?

The problem is that different technology platforms support different areas that drive engagement. Most likely these platforms are managed by different teams following a different process and, most likely each team is claiming success for each individual customer purchase.

Customer Journey Orchestration (CJO) works by linking together online and offline steps in a customer journey, and by doing so it enables an organisation to make relevant communications based around where a customer is in their buying cycle.

An example is a potential customer browsing an insurance website, and then calling in to ask further detailed questions, and to get a quote. The CJO technology will carry the browser enquiry over to the screen of the agent in the call centre, and then provide tips as to how to best proceed with the call.

CJO technology strongly supports the relevance quartile, and potentially the timing, when it provides follow up calls and SMS messaging where a sale has not been completed. To make it work, it needs the identification of value triggers, so that customers can be scored accordingly.

However, it will function much better when linked to a Customer Data Platform (CDP) as the underlying data management tool that will bring into play all that is historically known about the customer’s relationship, and from that it can, where relevant, also help with scoring and predicting customer preferences.

Another popular technology is called Customer Experience Personalisation (CEP) and this will focus more on incentives and timing. For instance, it will respond to triggers like dropped baskets, or geo-relevance when a prospect is near a retail outlet. CEPs often use incentives like discount offers, sometimes linked to timing countdowns, and social proof which is aimed at increasing a customers’ FOMO (fear of missing out).

Again, CEPs function better when provided with some longer-term data about a specific customer beyond that which they can pick up from their browsing behaviour. There is no point for instance in making a discounted offer to a customer with a history of buying at full price, or applying a timing countdown when the customer is showing evidence of churning.

Every business will set different levels of importance to the factors that influence engagement. Our recommendation is to start by focusing on the key customer experience factors that will make a difference to your business, and then to assemble the data and technologies that can implement an enhanced customer experience.

In order to get to the point where customers are presented with timely, relevant, contextual, and incentivised communications our expectation is that a CDP will always be in the mix as that will hold the deepest layers of customer information which it can share with the CJO and the CEP.

a CDP mixed with CJO and CEP

With technology enabled there are still the three further important factors; building a library of content, setting up a cross functional team, and developing the right culture. And one thing not to be ignored is introducing test and control for every change in the customer experience that you introduce.

Some of your tests will succeed, and become game changers, but others won’t. The culture bit needs to learn from both the successes, and the failures, and move on.

Unifying all of your customer data from your combined technology stack will support your strategy to become an experience led business.

Want to find out more about how a customer data platform works? Contact us to request a free demo.

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.

Will there be an avalanche of consumer demand after lockdown?

It’s most likely that there will be a massive surge in many areas of the economy after lockdown, including consumer demand. So now is the golden moment for companies to prepare for this.

We know first-hand that many companies are putting infrastructure development projects on hold at present, when logic suggests that they should be doing the opposite. So when the bounce-back happens they are fully capable of taking advantage of it.

Often the reason for delay is cash flow, as this is also a time of unprecedented financial stress for many companies.

So if you are thinking about getting your technology for marketing upgraded to be ready for when consumer demand picks up after lockdown, we can make an offer that could help substantially.

If your interest is in getting hold of a fully serviced cloud-based customer data platform (CDP) at a moderate price, then we can offer you a substantial license holiday of at least three months if you can place an order in April or May. The free period will continue until the lockdown is over so it could be extended.

A CDP is capable of supporting B2C marketing in many different areas including:

– customer message personalisation that is based on a complete view of online and offline customer activity
– multi touch order attribution that tells you how in combination all your online and offline channels are performing
– customer value and customer retention dashboards so that you can confidently calculate how much you can afford to spend on recruitment
– dormant customer reactivation based on propensity models that tell you who is most likely to respond
– one click campaign results reporting to give you up to date news on how your different contact strategies are actually performing
– automated outbound campaigns responding to online and offline triggers

We can also help you with making a decision about whether or not to invest by helping you build the business case for a CDP at no charge, even if you do not proceed to purchase one from us when the work is done.

The last two business cases we developed for B to C companies showed a return on UniFida technology of between four and eight times the cost.

Please do take a look at how our clients have benefited by using UniFida, and also email us with a time that would work for you for an initial call to see how we could help.

 

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.

Are customer value metrics the backbone of your marketing?

It’s intuitively obvious that they should be, but what may not be so clear are which actual metrics you need, and how to connect them to different areas of your business decision making processes. Let’s take four key ways in which you can take advantage of customer value metrics.

 

1. High-level business planning

Your turnover is equal to the sum of the customer value provided in any period. So, to look forward to how your customer value is going to be provided in the future you need to be able to project from your current customer base, remove those that are going to attrite, and add those that you are going to recruit.

The metric to support this is the average value per customer in each year since they were recruited. So how much value in their first, second third year etc. This allows you to very easily roll customer value forward for planning purposes.

When you start from your planned turnover in say next year, you can then tell how much of that is going to be provided by the exiting customer base, and how much will need to be provided by how many new recruits.

You will also want to apply some assumptions about how value is going to be altered by improvements to the way you look after your customers, and then you will have the basics of a customer-based business plan.

 

2. Understanding which customer groups provide what level of value

You will be very aware that not all customers are equal when it comes to their level of spend with you.

So, you will need to dissect your average customer value by the type of customer they are. Factors such as age, gender, and product categories purchased can all be used to profile the value of your customers.

The benefit then is that you will know what groups to target your recruitment efforts at.

 

3. Examining the customer value provided by different channels and media

This type of analysis leads you directly to understanding the ROI provided by different channels and media.

Indeed, we like to use a metric which is the amount of longer-term customer value derived from every £1000 spent in a particular recruitment mode.

You can undertake this at a very micro level, such as individual media, or more macro level, such as a channel.

There is though a caveat; many customers are now recruited as a result of contacts from multiple channels. However, this does not prevent you from looking at the customer value obtained from each recruit for whom the channel has played a part.

 

4. Where to focus retention?

This is a harder question to answer as your higher value customers will often be the most loyal.

What you need to know is which of your higher value customers are more at risk than others.

For this you will need an individual level predictive model for risk of attrition with which to score customers, and find the higher value, higher risk, group.

 

Some conclusions

  • Understanding all aspects of longer-term customer value is critical for every successful marketeer.
  • To achieve this, you need a single customer view that can track customer behaviour through time.
  • You will then need to be able to obtain the metrics.
  • It won’t come as a surprise to regular readers of our newsletters that our customer data platform UniFida has been designed to provide most of the metrics we have been describing on demand.

In some cases further analysis will be required, and our data scientists are happy to help with this.

 

If you would like to talk to us about how to get the customer metrics you need, then please email to say when and how you would like to be contacted.

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.

Customer Value Analysis

Furniture Retailer Explores Customer Value

The Situation

An upmarket UK furniture retailer uses customer value analysis to determine whom to recruit and how best to cross-sell.

The Client’s Business Goals

Initially the client, who had historically mainly monitored store and product performance, just wanted to understand the longer term value of customers.

We proposed extending the brief to looking at the characteristics of high and low value customers, and also the typical customer journey in terms of second and subsequent purchases so as to inform crm activity.

Our Solution

  • Step one was to assemble customer level records from very detailed transactional history, and in particular to join together multiple transactions into one sale when they were closely linked in time
  • Step two was then to count longer term value by recruitment time period (the data going back almost ten years)
  • We then matched the customer data to external demographic and lifestyle data in order to see if there were any characteristics that differentiated high from low value customers, or whether that was influenced by the store they used to make their first order
  • Next we looked the time between first and any subsequent orders
  • And finally we looked at patterns in the sequence of purchases to see, for example for all purchasers of product category A, what were their most likely subsequent purchase categories

Key Benefits

  • An initial and surprising finding was that it was not possible to differentiate high from low value customers by their demographics or lifestyle; however there was a major scale difference between high and low value customers at first order, which was not challenged by subsequent orders. This enabled the client to focus from the start on cross selling and retaining those with initial high value purchases
  • Our next discovery was that nearly all second orders came in the first year since first order, except for a small number of people buying replacement covers for sofas and chairs after a few years. This implied that follow up CRM activity needed to be initiated immediately after first order, and that it was not productive after the first year had elapsed.
  • The third key benefit was that the customer value analysis showed clearly what to cross-sell; we were able to find very distinct patterns in the types of product purchased at second order compared to first order.

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.