Moving from agency insight to client-side analytics

Andrew Orr progressed from insight analyst to insight manager at CPM International, a field marketing agency, where Samsung was one of his clients. He has now joined Samsung’s commercial marketing team as a commercial analyst. We asked him about the move and his advice for anyone starting out in analytics.

What does field marketing involve?

A brand pays a field marketing company to promote its brand or products, often in stores across the UK. It’s widely used for groceries and fast-moving consumer goods. At CPM, Andrew worked with a team of promoters who went into Currys, PC World and John Lewis stores to promote and sell Samsung products to customers.

Field marketing often includes a team of merchandisers too. They visit stores to make sure branded displays and products are clean and well presented. They also advise on where products sit on the shop floor, identify the highest-footfall areas and give store staff one-to-one training, so they can sell the products when the field team isn’t there.

How do you measure a field marketing team’s impact?

Analysts have regular access to a retailer’s point-of-sale data. That lets them see how stores without field staff are doing, and measure and rank them against stores that have staff in. Much of the work is comparing the field team with a control group of similar-sized stores without that support.

You look at things like average selling price, a promoter’s conversion rate (how many demonstrations it takes to secure a sale) and the training hours merchandisers deliver to store staff. Not everything has a monetary value attached, but you can still measure impact and success.

What changes when you move from agency to client?

The transition is much easier if you move to a client you’ve already worked with at an agency. Agency-side, you spend most of your time in the client’s office, so be careful not to lose touch with your own agency.

It’s important to balance working for your client with keeping an eye on your colleagues’ clients, so you keep up with best practice, trends and new methods.

Field marketing is heavily weighted towards groceries and fast-moving consumer goods. Samsung’s home appliances are consumer goods too, but they move far more slowly: people only buy big-ticket items when the old one breaks or they’re redoing their kitchen. So you have to be selective about what you pick up from analysts working with other clients.

What do people misunderstand about analytics?

You don’t need to be an expert mathematician. Simpler maths is often better because it’s easier to explain, especially to non-technical stakeholders. You won’t always be explaining a formula or method to someone with a maths background, so the simpler the better. What stakeholders want to know is the “so what”.

What advice would you give someone entering analytics?

Make time to upskill. There are plenty of online courses. Learning one of the big three data visualisation tools, Power BI, Qlik or Tableau, will help a lot when you present results. They are newer to analytics than Excel spreadsheets or PowerPoint reports.

Be clear about your skills and goals, so you apply and train for the right roles. You might think you want to be an analyst when a developer or data scientist role would suit your skills better.

Think beyond the numbers. Some people are great at the mathematical side of analytics: they know exactly which formula to use and how to reach the right answer, but struggle to move past that point. You need to ask “where do I go next?” and turn the data into actionable insight.

It’s a storytelling role. It helps business leaders make better decisions based on your recommendations.

Andrew Orr

You don’t need a maths or statistics degree to be a successful analyst. We’re seeing more people than ever from law, English and other backgrounds. What they need is to be curious, a strong communicator and keen to apply themselves.

How do you see analytics tools evolving?

Companies want to get better at using their data to make decisions. That means adopting data visualisation tools, not only to visualise the data but to automate the process. It’s a huge time saver, and frees you to work on other things, like gathering feedback and understanding the reasons behind the numbers.

Many companies will also build separate data science teams, because what an analyst does is very different from what a data scientist does. Data science uses mathematical modelling to try to predict the future: to spot what might become a hurdle for the company, or something it needs to act on quickly. Those teams are likely to become more separate from analytics, while analysts work more closely with other departments such as product and marketing.

For more on the communication side of the job, read Stakeholder management – the new must-have skill for data analysts. The Career map shows how analytics, data science and insight roles connect.