One thing we have learned over the last few years is that we’re being asked to achieve more, with less (Getting more bang for your buck). We’re facing increasing competition globally for products and services; more niche companies emerging online that provide everything the consumer wants at a lower cost base; consumers are expecting us to provide relevant offers to them, no matter how deep our relationship; new products or services being released by our own organisations; internal competition for share of our customer and prospect wallet; and often with tightening budgets.
With each of these factors affecting our marketing
planning we often find ourselves struggling to reconcile the effect of each of
these levers, requirements or constraints across our campaign planning. We
create priorities depending on who shouts loudest or has the biggest budget, or
what is subjectively perceived to be the next best offer.
Marketers are seeking innovative ways to acquire, retain
and grow customers. The situation is exacerbated
by a growing number of customer touch-points, more granular audience segments
and more product and offer permutations.
The temptation is to send more messages to more customers in the hope of
achieving better marketing results. This
is a particular challenge for companies that have a large number of competing
communications they want to send to the same or overlapping sets of customers.
This situation raises a number of business challenges:
With fixed channel capacity, what
channels do I use to communicate with which customers and when? How do I avoid overloading customers with too
many messages in a short time?
With limitedopportunities to
communicate, which offers do I give to which customers while managing
contact fatigue? How can I choose the
best campaign for a customer from among several that are overlapping?
Which customers get offer X, when there are only
a limited number to distribute?
How do I achieve the maximum expected return on my campaigns given
Which offers conflict with each
other? Which offers do I give only if
another offer has already been presented?
Can I avoid customer base cannibalisation by multiple campaigns run by
different lines of business?
How do I allocate my money across
different campaigns, offers, or customers?
Can I design and proactively manage my customer contact strategy, rather
than just letting it happen?
How do I ensure customers who elect to not
receive email are not contacted by email?
What is the best way to enforce cross-campaign policies (opt-outs, DNC,
Contact optimisation is a mathematical approach
for identifying the best combination of messages for each customer from among
competing options, while complying with business objectives and constraints, with
the goal of targeting the right customer, with the most attractive offer, at
the optimal moment, using the most strategic channel.
This, however, is a complex problem to solve. Customers x
channels x offers x time options creates potentially
billions of variable combinations. To
add to the complexity, marketing organisations are also constrained by business
goals and restrictions, such as revenue targets, contact policies and budget
Under these pressures, organisations will use a variety
of methods to get the best results, all of which are perfectly valid, but will
often result in significantly different rates of success.
To demonstrate the pros/cons of some of these methods,
I’m going to use a fictional organisation, the Purple Supplies Company (PSC).
PSC doesn’t have many customers (10) or many campaigns (3), but I hope it’ll demonstrate
PSC’s data scientists have scored each customer on their
likelihood (out of a maximum of 100) to take up the offer presented in each
campaign as part of their daily data preparation activities. They’ve chosen
this simple propensity score for consistency, but could have chosen any metric
of success, such as potential revenue.
We’ll assess each campaign’s potential effectiveness against each “prioritisation/optimisation” method used and compare them using a per campaign and overall score.
We also have some very simple business constraints:
customer can receive one campaign only
campaign has a minimum of 3 and maximum of 4 recipients.
These are applied to ensure we don’t over market to an
individual or leave any campaigns without enough recipients.
First run, First out the door
This approach is effectively a random based selection on the first campaign to run that day. It may use elements of customer scoring but is most likely to choose the first data records it hits in the database are output. The resulting Campaign scores are arbitrary and is simply based on the first 3 or 4 customers available in each campaign and depending on the order they are run in the day will create significant variations. Thankfully this approach is increasingly rare, but we do still see it on occasion.
In this case we prioritise our Campaigns and select the best customer(s) for each Campaign in order. The target recipient for that campaign is the highest scoring customer within the campaign. Whilst this approach is likely to produce good results for the higher priority Campaigns, it does result in poorer results for the lower priority communications as they will typically be left with the lower scored customers to choose from. i.e. the higher the campaign ranking the larger pool of customers they get to cherry pick. In this instance Campaigns are taken in the order A > B > C. In the example below, Campaign C can only be sent to customers 1, 5 and 8.
This switches the emphasis on to the customer first. In this approach, each customer is evaluated row by row, evaluating each Campaign in order to select the highest score campaign for that individual. This can mean however that the higher up in the data the customer is, the more likely that they will get a Campaign offer they are likely to respond to as they have wider pool of campaigns to select from. As you can see in the data set below customers 9 and 10 must receive Campaign C as they would otherwise breach the minimum number of recipients per Campaign. The further down in the customer list an individual appears, the fewer campaign options they are left with.
Marketing Contact Optimised
This approach evaluates all of scores, rules and constraints and reduce the bias of Customer or Campaign. Optimisation algorithms apply all the logic, across all the data at the same time and are designed to identify the best combination of all the factors in order to provide the maximum output score. It can be difficult to determine which factors have greatest influence, and the introduction of one rule can significantly impact the output results, but always with the same goal.
Even using this simplistic model, this approach results
in an 8% uplift on the best performing alternative method and 12.5% uplift on
the worst performing method. Convert this to the financial measures of your
organisation it could result in many tens of thousands of pounds/dollars in
Whilst it’s easy to see and evaluate the impact on 3
Campaigns and 10 Customers, it’s much harder to do this with millions of
customers, tens of campaigns, all creating a significant number of permutations within a day
There are however some organisational challenges to
implementing a more optimised approach.
Many marketing organisations we have worked with are
swayed by the demands of product managers, their requirements push the highest
scoring customers to the top of their campaigns, however this is likely to have
a negative impact on other campaigns and the business overall. Changing the way
product managers are recognised and rewarded is key to ensuring that the
organisation benefits. It is important to set targets, but not at the cost of
other business units.
Other organisations struggle to score their data at a
meaningful level, whilst the most optimal approach is to score every offer,
against every customer, this can create a significant burden on the data
scientists. Starting at a high product/category level and working down through
the product hierarchies will result in improving scores and optimisation over
It is tempting to try to create the perfect optimisation
model for the organisation on day one, however this activity can take many
months, if not years, to evolve and will never be completely “perfect”. Most
successful optimisation projects start with a limited scope to prove the
concept and build confidence in the solution, followed by incremental
deployment, adding new rules, campaigns and scoring as the business learns and
Investing time and resources, addressing these challenges
and embedding marketing optimisation into the marketer’s business process will
always lead to overall campaign effectiveness.
Contact us to find out how Purple Square can help you with your marketing optimisation initiatives.