How insurers can use deep learning to boost performance marketing

16Dec - by aiuniverse - 0 - In Deep Learning


Anyone looking for evidence that insurance advertisements have become a mainstay of popular culture need look no further than Geico’s YouTube channel, which currently boasts 1.88 million subscribers and is home to videos with tens of millions of views. Geico’s competitors might not have as robust a presence on the platform, but their brand representatives (Jake from State Farm, Allstate’s Mayhem, Progressive’s Flo) are as familiar to the public as the Geico Gecko.

For these insurance companies, spending vast amounts of money on advertising on both traditional and digital channels is a must. However, having such universal brand recognition also makes it difficult for brands to know whether a particular person requires more exposure to advertising messaging in order to convert or whether that person has already been shown sufficient messaging to make a decision.

At the moment, most insurance companies are erring on the side of caution, preferring to inundate audiences with additional digital advertising instead of strategically focusing their spending on the people with whom it would make the most difference. With deep learning, insurance companies can drive down the costs of customer acquisition while improving the effectiveness of their advertising, enabling them to win away more new customers in a highly competitive market.

Optimizing digital advertising

What factors might induce someone to choose one insurance provider over another? The amount of the quote and the quality of the services provided absolutely play a part, but in the crowded world of insurance, where individuals have a multitude of companies to choose from, each of which offers a relatively similar roster of products for more or less the same price point, advertising and branding can play a crucial role in swaying someone to choose one provider over another.

Insurance companies understand this, hence the plethora of witty, exciting, and just plain weird ads that inundate the airwaves regularly. But what they lack is the ability to tell which type of person is more likely to pick their product over another. Considering that anybody possessing a home, car, motorcycle, etc. is a potential target for an insurance provider, being able to hone in on the specific traits that differentiate a future Progressive customer from an Allstate devotee is an incredibly valuable skill that helps brands save money while improving the likelihood of a successful conversion.

Thanks to technological advances, insurance companies can now recruit deep learning’s analytical capabilities — its ability to find discrete patterns within customer data that might hitherto have remained undetected by human marketers — to optimize their digital advertising and targeting. Deep learning allows insurance companies to differentiate between people who are already customers, people who are not yet customers but are amenable to conversion, and people who will not be swayed regardless of how many Limu Emu ads they are shown, thus enabling brands to focus their advertising spend on the most likely prospects and avoid wasting money on fruitless pursuits.

In other words, insurance providers can use deep learning to focus their advertising on the types of people who are most likely to be attracted by a brand’s unique proposition, whether this happens to be the particular value an insurance company is able to offer or the brand ethos the company has carefully cultivated.

Insurance providers can enlist a deep learning-enabled algorithm to examine all of the existing customer data they have on hand and combine it with available third-party data like demographics. From there, the algorithm will find similarities between existing customers, then identify people in the general population whose characteristics mark them out as good candidates for conversion. The benefit of relying on deep learning to carry out this search, as opposed to more manual marketing methods, is that the deep learning algorithm is not limited to a single understanding of what a customer looks like; instead, the algorithm is capable of identifying infinite combinations of characteristics that might distinguish someone as a potential customer. In some cases, what the algorithm identifies as a valuable audience cluster might run counter to what marketers believe to be their core consumer base, thus presenting brands with the opportunity to reach a previously untapped audience.

A solution for common insurer problems

As has already been mentioned, deep learning offers insurance companies the ability to hone in on those who are still on the fence and figure out the best way to convert them. Cognitiv’s research has found that the implementation of deep learning, in addition to delivering higher rates of incremental lift than other solutions currently on the market, is also capable of increasing conversion rates and improving ROI on incremental customers. For the insurance industry in particular, which has long struggled with incrementality and cost of acquisition, the existence of such a solution will help providers map out more effective, sophisticated marketing strategies that focus on reaching the right audiences in the manner most likely to lead to an efficient conversion.

Insurers spend so much money on advertising, often without the assurance that their blanket of ads is having the desired effect of swaying undecided insurance seekers. Many companies have tried for years without success to accurately measure incrementality and attain incremental lift on the scale they require. Deep learning finally offers a solution to those problems. By training a deep learning algorithm on insurance providers’ own first-party data, marketers can gain a better understanding of what their customers look like, and target others with similar characteristics while avoiding sending ads to existing customers or people who have already made their mind up. Diehard Geico fans need not fear, though: the Geico YouTube channel will always be there when they need it.

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