Sponsorship measurement and logic models

logic model bw

We won’t pretend : this article on sponsorship measurement and logic models fails every element of originality except one. It’s 99% based on a great article from Financier Worldwide by Paula Papp, Laura Petschnig and Kalina Kasprzyk from Frontier Economics on measuring innovation. The parallels are overwhelming so we’ve wholeheartedly plagiarised their article, with great appreciation. The 1% of originality is the application to the world of sponsorship.

Sponsorship is notoriously hard to measure. Articulating it, managing it and monitoring it are challenging tasks in any industry. But logic models – a tool from the world of public policy – can provide clarity among the confusion. Here, we look at why sponsorship measurement can be so tricky, and how logic models can help.

The challenge of sponsorship measurement

Most sponsorship programmes are multi-faceted. Even when they have a lead objective, they will also include a range of secondary objectives. These will be interlinked in various ways – from improving customer satisfaction, through to generating new business or enhancing cost efficiency.

Different parts of the organisation – product or geographic divisions – will often have slightly different needs. And to complicate things further, sponsorship projects usually encompass both short-term, incremental change and long-term, transformative disruption. Put simply, it is a lot to manage – and to measure.

Measuring progress is vital

A sponsorship programme often takes the form of a portfolio – diverse initiatives that mature at different paces. That means a well-implemented sponsorship measurement system is necessary to evaluate performance.

After all, businesses need to: identify successes and failures; determine when to halt policies that are not working and accelerate those that are; promote initiatives internally and incentivise teams to carry them out; and report overall progress to top-level management.

The benefits of a system that does all these things are obvious. But implementing such a system is far from easy.

Metric setting is critical

One of the biggest hurdles in measurement is the challenge of establishing a set of metrics that can provide big-picture clarity on overall progress, while also providing sufficient detail about each individual initiative.

The level of difficulty increases further when you need to monitor slow-burn, long-term sponsorships alongside short-term objectives that produce outcomes from day one.

Example : the UK retail bank

These difficulties are illustrated by the case of a major UK retail bank. The company had a mix of sponsorship initiatives in progress, aiming to meet a number of high-level goals – like growing share in current accounts and increasing brand preference.

To evaluate performance, it was tracking a broad set of metrics. These included high-level indicators like NPS, and more specific metrics like the number of people signing up to new apps or services. So far, so good. But with this mix of measurements to manage, and with several parts of the business involved, it wasn’t enough.

The high-level metrics were affected by multiple initiatives, so it was hard to define causal links. There was no clear connection between the overarching metrics and the more specific ones, and there was a gap in capturing intermediate results for long-term sponsorships.

What is more, while established metric types were suitable for some objectives, they were proving less effective for others. The business had seen 35 percent growth in its insurance products, for example – but how could it tell how much this was down to its sponsorships?

Logic models: what are they?

It was clear that the bank needed more clarity. To take a step back and apply some big-picture thinking, the perfect tool for them was logic models.

A logic model is a step-by-step pathway, outlining how to move from input to impact. It is considered a best-practice tool in measuring the outcomes of public policies and can be very useful in commercial contexts too.

The model breaks down evaluation into steps, from initial inputs, to the activities they generate, and then to the outputs, outcomes and impacts produced. The basic structure of a logic model looks like this: step 1: inputs – resources used (usually time or money); step 2: activities – initiatives carried out using the inputs; step 3: outputs – things that result from the activities (usually short term); step 4: outcomes – things that result from the outputs (usually medium term); and step 5: impacts – changes that ultimately result from the outcomes (usually long term).

It is a helpful way of outlining the causal link between each step, giving businesses a greater sense of control over the entire process.

Logic models: how do they help?

Logic models help to guide sponsorship programmes at all stages: implementation, monitoring and evaluation of outcomes.

Perhaps most importantly, they make sponsorship measurement much more straightforward – especially as different stages of the model tend to suit certain metric types. Inputs and outputs are best tracked using directly measurable units: resources like money and hours for inputs, and key results for outputs. Outcomes are best measured as new or changed behaviours, observed from customers or employees. And impacts are best assessed by overall results for the business, such as revenue generation or brand shift.

They can even highlight existing behavioural barriers, where nudges may be needed, such as incentivising customers to overcome the effort barrier of downloading an app.

The UK retail bank

A logic model was developed for the area relating to brand and sales impact, as the most significant target outcome.

The model itself emerged from hypotheses generated by existing research. At the heart of the model was an engagement ladder, a series of steps and metrics which the broader team believed would lead to a position of high satisfaction with the brand.

This model enabled us to retro-engineer sponsorship measurement practices and over time a positive correlation emerged with current account openings. The process helped consolidate confidence in the sponsorship and its impact – and helped our client to focus on initiatives which would deliver against the individual steps in the engagement ladder.

Clearer sponsorship measurement

Experience shows us that logic models work particularly well in managing and monitoring sponsorship and for this reason should be considered a useful tool in both sponsorship planning and evaluation. They allow organisations of all types to obtain a clearer picture, and greater control over their often complex sponsorship projects.

Logic models help to provide clearer direction on how to measure different objectives, replacing blurred boundaries with logical steps.

This means clearer reporting, communication and target-setting, from day one to the long-term future – all of which are outcomes that any organisation implementing sponsorship can get behind.

Quoted without permission but great appreciation from Financier Worldwide, May 2021.