A professional beauty distributor and five journeys for two thousand salons

Who the client was and what was getting in the way

The client distributes professional beauty products to hair salons, beauty studios and spas, with more than two thousand business contacts in its database. The sales team handled all communication with these contacts, manually.

In practice, sales representatives sent hundreds of messages every month about offers, new products and promotions. Each person did it a little differently, so customers received messages based more on who managed their account than on what they needed. Everyone got the same content, regardless of salon type, purchase history or interests. Hair salons and beauty studios buy very different products, yet received identical emails.

Another problem was customers who stopped ordering. If someone had not placed an order for three or six months, they simply disappeared from view; there was no mechanism to win them back. New enquiries from forms also waited one to two days for a reply. For a salon buying supplies, that was enough time to order elsewhere. The sales team was drowning in routine work instead of closing deals.

What we built

We started by splitting the database into segments by business type: hair salons, beauty studios, spas and treatment clinics. We also added a customer-value score based on actual behaviour, including email opens, clicks and website visits. Interest no longer had to be guessed by a sales representative.

On that basis, we built five journeys in GetResponse. The first onboards a new customer through a series of seven emails, from the first order to an ongoing relationship. The second re-engages inactive customers when they stop ordering. The third recommends complementary products based on what a customer has already bought. The fourth follows up after an order. The fifth is a regular product newsletter.

Content is tailored to each segment. Hair salons receive hair dyes and hair-care products; spas receive massage and body-care products. Email content blocks also change according to purchase history. Finally, we connected the system to sales: customers whose behaviour signals interest are routed to the sales team with an alert, and the team receives engagement reports.

How it works today

After a first order, a new customer enters a seven-week email series that runs without human involvement. A customer who has been inactive for several months receives a re-engagement campaign. Someone who has just bought receives a complementary product recommendation instead of a random promotion. Sales representatives get in touch when the data signals interest, so they can focus on conversations that are more likely to close.

Implementation took ten days. Three were spent reviewing the database, mapping the customer journey and designing segments. Five went to configuring and building the five workflows, along with the customer-value scoring. The final two covered launch, monitoring the first campaigns, comparing subject lines and training the team.

What changed

Sales rose 20% year over year from the same lead database. That is the most important result in this project: the client did not buy any new contacts. More than two thousand business contacts are now managed automatically. Five journeys are running: onboarding, relationship development, re-engagement, complementary-product sales and a newsletter. Customer relationships continue to develop without routine work from the sales team.

What we would do differently

We should have cleaned the database before launching the journeys, not during rollout. A database of two thousand contacts collected over the years contains outdated and duplicate addresses, as well as contacts who have not responded in a long time. Turning on five automations at once for an unorganised database immediately affects deliverability, which can undermine even the campaigns that are working well.

Second, we should have launched the re-engagement campaign on its own, a week before the others. It is the best test of database health and quickly shows how many contacts are no longer reachable. Our third lesson concerns customer-value scoring. We based it on email engagement because those data were readily available, but order frequency is the strongest signal for a distributor. Connecting sales data to the score would have made it more accurate, and we should have done that earlier.