Only by intelligent data processing can businesses be confident their sales and marketing efforts are reaching the right audience at the right time, says Harvey Sarjant, UK MD SirData
Whatever your business, you’re likely to be increasingly reliant on your own customer data to drive your sales and marketing operations. Ensuring your own first party data is up to date and maintaining the latest intelligence on your customers – present and past – is vital. Onsite and your CRM, should be the basis to retain new and live accounts.
Online retailers, for example, generate lots of historical purchase data, which they then use to ‘re-target’ former customers. This can work well, but it has a tendency to narrow down audiences rather than attract new ones, and can prove annoying for former customers.
To target new customers, you can buy data from external sources relevant to the audiences you are trying to reach. The problem is that, unlike your own first party data, you have limited information about it in terms of how it is collated, how accurately it has been segmented and how old it is. Even if there are some trade bodies such as IAB Tech Lab and Data Marketing Association who offer guide lines and test clusters, it can often be a rather hit and miss affair, which is far from ideal when you’re basing a sales and marketing campaign around such data.
This is a dilemma that many companies and their marketing teams are currently facing. They are caught in a battle between data accuracy and scale. Add to this the ongoing frustration over the lack of transparency that exists in what makes up third party data and you have all the ingredients for a growing confidence crisis over the suitability of consumer data, something that is currently undermining sales and marketing efforts.
The good news is that the amount of consumer data will continue to grow, but companies need to be smarter in the way they process it to get the most from it and help validate its accuracy. It is no longer good enough to rely just on relevancy and recency – the seemingly two critical factors in determining data quality. Looking at customer behaviour from the beginning of the purchase funnel through to the end, what patterns appear, understanding down to an individuals purchase behaviour, who are they buying for, the emotional buying patterns, why they buy, but also why they didn’t buy are becoming so important.
The answer lies in how you process the data – either by your internal team if you have the skills in house or by a specialist data processor. The prevailing approach tends to be stalled and expensive, not knowing where to start, who to hire, what data to process, what technology is required through to undefined data strategies and objectives. However, using specialist data processor companies overcomes all those hurdles, you could be targeting ‘custom built audiences” with media activity pretty quickly.
So how can data be used to gauge someone’s position along the buying journey, and establish their likelihood to make a purchase?
The answer is ensuring;
– first, data sources are gathered collectively rather than in isolation and must be aligned with a propensity score.
– second, you are looking outside for a variety of key data points that will help understand human purchase behaviour, again scoring the intent actions.
– thirdly, the data is intelligently processed with key objectives and goals in mind, then delivering those audiences in real-time in order to target them with a unique value proposition
Combining and processing customer data in this way enables individual contacts to be scored in terms of their likelihood to buy a particular product or service. The high scorers are essentially the hottest leads, but the scoring system provides a spectrum of data cascading down from those with the highest level of intent. Depending on the scale requirements of your campaign, you can lower the score and this lower level of intent all the way down to ‘interested in’….but still be sure that you are targeting the right consumer, they just happen to be higher up the purchase funnel.
Essentially, this approach turns isolated and historical data into a real-time behavioural event, only this approach will pay dividends in terms of significantly improving the performance of your sales and marketing activities. Data can be as big as you like, but if you can’t establish the intent to purchase, then you’re not getting the best possible return nor maximising your sales potential.
About the author
Harvey Sarjant is UK managing director at data processing business SirData
