Programmatic Conversion

Programmatic marketing involves data driven insights to convert prospects into customers. There is more than meets the eye in the case of conversion rate optimization. Some of the deciding factors for conversion are UX design, the landing page, the source of web traffic, content, competitive price of products, good will, social media marketing, effective campaigns and customer engagement. Programmatic marketing entails analsying data at every customer touch point and targeting the consumer with compelling, preferably  personalised, offers. Conversion is not necessarily making a customer shell out money, it could be interpreted as winning customer loyalty by means of signing up for newsletter, downloading whitepapers or trial versions of the product or spending considerable time on the site. This loyalty, in the long run, could result in big wins through persuasion in the form of emails, SMSs, direct contact and targeted recommendations.

Channelizing data about prospects – online behaviour, previous shopping, socio-economic segmentation, online-search, products saved in the online basket, in other words getting to know the customer better to be able to suggest meaningful differences in people’s lives through the products on offer, results in higher conversion rates. It is here that digital convergence is of paramount importance. Digital convergence blends online and offline consumer tracking data over multiple channels to come up with targeted campaigns. Offline tracking through beacon technology is catching up. It is a win-win solution for both the retailer and the consumer providing each with useful information, the consumer, with an enabled smartphone app within a certain distance from the beacon, recieves useful and targeted information about products and campaigns and the retailer gathers data about consumer shopping habbit.

The online experience can be enhanced to reduce the bounce rate by incorporating some of the following design thoughts:

  1. Associative content targeting: The web content is modified based on information gathered about the visitor’s search criteria, demographic information, source of traffic, the more you know about the prospect, the better you can target.
  2. Predictive targeting: Using predictive analytics and machine learning, recommendations are pushed to consumers based on their previous purchase history, segment they belong to and search criteria.
  3. Consumer directed targeting: The consumer is presented with sales, promotions, reviews and ratings prior to purchase.

Programmatic offers the ability to constantly compare and optimize ROI and profitability across mulitple marketing channels. Data about consumer behaviour, both offline and online, cookie data, segmentation data are algorithmically analyzed, to re-evaluate the impact of all media strategies on the performance of consumer segments. Analyzing consumer insights, testing in iterations, using A/B testing contributes to a higher conversion rate. Using data driven methods to gain a higher conversion rate is programmatic conversion and it’s here to stay.

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Recommendation Systems

Recommendation systems have changed the way people shop online, find books, movies or music, news articles go viral or find friends and work mates on Linkedin. The recommendation systems analyze the browsing patterns on websites, ratings or most popular items at that point of time or the products saved in ones virtual basket to recommend products. Similarly, the common interests, work skills or common geographical locations are used to predict people, that you might want to connect with on social media sites.

Behind such personalized recommendation systems lie big data platforms including software, hardware and algorithms that analyze customer behavior and push recommended products, in real time. The big data platforms handle both data and event data distribution and computation. Data can pertain to how customers or customers similar to the one in question, have rated products in the past while event data could be tracking mouse clicks that trigger events for example viewing a product and sometimes both of the above need to be combined to be able to predict a customer’s choice. Hence, the recommendation system architecture caters to data storage for offline analysis as well as low latency computational needs and a combination of the two.

The data platform architecture needs to be robust enough to ingest continuous real time data streams into scalable systems like Hadoop HBASE or any other big data data storage infrastructure like AWS Redshift. Apache Kafka is usually used as the messaging system for the real time data stream in combination with Apache Storm. Due to high throughput data redundancy needs to be taken care of, in case of failures. If the real time computation needs to take into account customer data like previous purchase history, preferences, products already bought , segmentation based on socio economic demographics or data from ERP, CRM, in that case either all the systems have to be available online to be able to blend the data in real time or the customer detail data could be mashed up, offline to create Single Customer View and queried in combination with the real time event data.

The valueable assests of any organisation are customers,products and now, data. Machine learning algorithms combine the three assets together to leverage business gains and predictive analytics is imperative in being proactive to customer needs. Some of the algorithms used for recommendation engines are content-based filtering, collaborative filtering, dimensionality reduction, Kmeans and matrix factorization techniques. The challenge is not the data storage, with wide availability of highly scalable data storage platforms, but the speed with which the data needs to be analyzed in case of recommendation systems. The best approach is to combine mostly precomputed data with fresh event data using pre modelled algorithms to push personalised recommendations to the customer interface.