Specsavers worked with digital agency MG OMD on a paid search campaign which encouraged consumers to book eyecare appointments online. Keyword bidding was based around data on appointment capacity at Specsavers’ stores, rather than geotargeting.  This helped maximise coverage across the UK and deliver tailored messaging at scale with minimum CPA wastage. It also helped Specsavers futureproof its marketing strategy against the need to rely totally on first party data, which is increasingly regulated. Collated in a cloud-based tool, the data was fed into the paid search campaigns on an hourly basis, with AI-powered bid modifiers cost effectively optimising engagement. The campaign boosted store appointments by a third, for a quarter of the cost.

MG OMD was briefed to increase store appointment numbers through paid search, whilst reducing cost per acquisition (CPA).

Specsavers consumer data showed that a standard paid search campaign which targeted users by postcode would involve wastage. It did not take into account how far people were willing to travel, contrasting city centre locations where several stores were available, with rural areas which required more travel time to access a store.

Instead, the strategy was to base the campaign around collating and centralising appointment capacity data and feed that into the ad bidding. This would help solve a key business challenge based around managing demand for appointments across Specsavers’ 900+ stores. Too much demand led to increased waiting times, which negatively impacted customer experience, while too little demand resulted in wasted resources and missed income.

Data on appointment availability was gathered from every store and centralised in a cloud platform. Each store was ranked in ‘high’, ‘medium’ or ‘low’ categories, based on their capacity. The data was refreshed and pushed into the search platforms hourly. Budget allocation and personalised ad copy was then based on location and appointment availability, which minimised wastage and maximised media return.

The data was fed into Google’s auction time bidding algorithm, removing the need for manual changes to bid modifiers, which were more time intensive. This also enabled different targets to be set which were based on appointment availability.

A/B testing minimised confounding variables such as demand volatility, seasonality, and competition.

• 34% increase in store appointments – which equates to an incremental 400,000 increase in store appointments
• 23% decrease in CPA