User web behavior is a key source of information for all companies seeking to engage with customers in the digital world. In this digital economy, it is imperative for organizations to optimize their marketing campaigns so that they can resonate with the right set of the audience through the right channels with the right type of content optimized for each target segment.
One of the major chunk of marketing campaigns are being run on digital platforms in the form of advertisements. As more and more organizations are designing innovative advertisements it is getting difficult to make your advertisement stand out from the rest of the lot.
To carve out a space for their advertisements, organizations are increasingly embracing data analytics to optimize digital advertising campaigns and squeeze as many insights as possible available from the web. By customizing advertising campaigns according to the audience experiences, organizations are witnessing a sizeable increase in the response rates thus creating their own place in the highly competitive market.
Customizing digital advertisements is not easy. You have to analyze a lot of user behavioral information like URL clicks, impressions, different touch points and data from devices, conversion rates, and the whole online user journey. Some of this information is unstructured in nature which has to be used alongside the structured user information in order to draw insights from the available data.
One of the data analytics technique which is widely gaining acceptance in order to deliver customized digital campaigns is look-alike modeling. To understand look-alike modeling better let’s look at an example: Suppose you have a customer A which generates highest revenue/profit for you. Now your company has to run marketing campaigns to acquire new customers. You have a list of people (target audience) whom you have to target through the marketing campaign. If you use a look-alike model it will find the attributes of the customer A (who is your ideal customer) like age, the frequency of visits per year etc. and then it will match these with the attributes of all the people present in the target audience. This way it will find out the people in the target audience who are most similar to your customer A and thus are most likely to become your most profit generating customer. So basically you have just found the look-alike of a particular type of customer. In simple words, look-alike modeling finds groups of people (audiences) who look and act like a set of customers with known behaviors. To learn more about look-alike modeling watch this webinar
One of the largest internet and media company which handles various portals and websites was trying to optimize its digital advertising campaigns by delivering customized ads to different target segments.
The company had a list of existing customers who got converted by recent advertisement campaigns along with their behavioral data. The company had to run advertisement campaigns in the future and serve relevant advertisements to different segment of customers. So they collected unstructured behavioral information of the existing customers using an open source stream processing software platform known as Kafka and ingested the pile of data into a Hadoop cluster where the incoming unstructured data was transformed and structured using a cluster computing framework i.e. Spark engine. This information of the existing customers was matched with those with that of the target population. With this, the company identified the look-alikes of the existing customers amongst the target population and targeted the look-alikes with the same advertisement that was used to target the already existing similar customers. As a result, this led to a more pinpoint and precise advertisement campaigns with a possibility of increased conversion rates
Do you want to run targeted marketing campaigns and enjoy increased campaign conversion rates? Then reach out to us at firstname.lastname@example.org