Whatever the Data Source, Find Lookalikes Like a Pro

Twins Julius (Arnold Schwarzenegger) and Vincent (Danny De Vito) may not look alike, but they have a lot in common. Twins, 1988, Universal Pictures
Twins Julius (Arnold Schwarzenegger) and Vincent (Danny De Vito) may not look alike, but they have a lot in common. Twins, 1988, Universal Pictures

Many companies use lookalike modeling to bolster customer marketing and advertising efforts. Social platforms, like Facebook and Twitter, have rolled out native tools to help businesses do just that: target customers who “look” a lot like their current customers. But what if your customer base isn’t active on Facebook or Twitter? What if you want to seize offline as well as online advertising strategies?  

To find your best audience you have to know what makes them tick, and to stay in business you need to find more that tick like them. More often than not, that information lies beyond first party and social data. Depending on how familiar you are with your target audience any of the following approaches using first, second and third party data can help you suss out new leads.

 

1) You know your customers and want more like them: Send your customer lists to a third party, like Experian or infoUSA. They will generate a slew of new attributes from syndicated datasets to help you broaden your understanding while isolating consumer trends. Augmenting a customer list with new yet targeted data can fill in major personality and behavioral gaps that social can’t, like community involvement or attitudes toward technology.

2) You don’t have a customer list, but you know exactly who they are:  Never fear: without a list you can still use lookalike modeling. Using second or third party data, research a customer with a handful of known desirable traits, like mid-career professional, female, between ages 35-50. You don’t need to know a lot about your customer to generate a rich understanding of them. Researching a handful of characteristics will give you a broad picture of your target demographic which you can then break down into different target audiences characterized by commonalities like geography, demographic, media preferences and more. Starting from such a blank slate frees you to create a wider array of advertising strategies for more nuanced audiences.

3) Use observed customer behavior data, rather than a customer list: Banks, grocery stores, clothing retailers all have the ability to collect shopping data. Where do people linger in-store? How much time is spent evaluating before buying? Use your behavioral information to define your best customers and find more like them. Grouping your customers by behavioral commonalities sustains growth because you’re leveraging the product’s underlying value proposition, which is likely to transcend different demographics. That said, once you hone in on particular audiences use external data to learn more about them.  

 

Each of these strategies delivers awareness about your customer outside of social’s narrow scope. Bulk pet food isn’t purchased in isolation. Big data has the ability to flesh out your customers into whole people so you can better position your brand and its value.

 

If you want to hear how Rhiza is helping industry leading companies find their lookalikes, send us a note.