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Online activity has the unique quality of producing reams of data, some of it invaluable and some of little use. Whether it’s an online storefront or a Facebook page, every single user action can be recorded and used for media analysis. Browsing for a new mobile phone, sharing a tweet, posting a vLog, or responding to a social media post… these are all activities that can be saved, tracked, measured, and analyzed in order to understand online user behavior.
Basic marketing solutions enable business owners to track and analyze all of this data and then use it for formulating marketing strategies. As technology has improved, however, this type of marketing is proving to be just one side of the online marketing coin. It’s quite reactive: users act, marketers analyze, and decisions are made. Those decisions, however, are based on old information. Audience engagement campaigns reflect what customers have done… not what they will do. Enter predictive media analytics.
The main goal of online marketers is straightforward: how can we make more money? Traditionally, marketers use media monitoring, viewer consumption analysis, demographics analysis, and channel conversion ratios to understand which advertisements, campaigns, and channels yield the highest conversion rates for specific audience segments. Successful initiatives are boosted, weak ones are culled, and the cycle goes on and on. “Successful initiatives” are characterized as rating high on the customer engagement scale — the content, and its delivery, connects with potential & existing customers.
The concept of predictive media analytics is to not only understand who your customers are, but being able to predict what they are going to do based on that analysis. “What are they doing?” takes a backseat to “What will they do?”
Data Science Studio (DSS), from Dataiku, is a robust predictive analytics solution capable of accepting data from diverse sources, cleaning the data, and then using powerful algorithms to understand what your customers are going to do given your specified parameters. Dataiku DSS’ advanced data wrangling features can be used to combine structured (e.g., web analytics) and unstructured (e.g., social media posts) data sources to get a 360° view of your clients. Understand exactly who your customers are, what they typically do while online, and how they prefer to engage with your company.
Dataiku DSS lets you use and compare a broad range of algorithms, from simple linear models to complex ensemble methods, to build the best predictive media solutions for your needs. Post-modelling, the software’s automated deployment & data automation features enable your company to quickly move your models to real-world business applications. For example, you may wish to build a scoring solution that uses Website and CRM data to predict which customers are most likely to purchase luxury women’s accessories next week. For retail storefronts, you may even want to include other dimensional data in your model—such as traffic and weather patterns—in order to fine-tune your campaign’s timing & delivery.
As mentioned, the core challenge of effectively reaching out to customers is engagement… which means relevant content. Content needs to be timely and meaningful to your customers. Predictive content analytics is all about being able to distribute content that fits perfectly with your current marketing needs; this means that other factors, such as context and related material, are used when disseminating your message. Dataiku Data Science Studio facilitates the placement of relevant content by performing a holistic analysis of your content management situation. Multiple factors need to be considered:
Dataiku DSS helps you to understand how your content can produce the greatest impact. It does this by looking at the big picture of your content management needs and enabling you to refine your messaging accordingly. For example, teams of data scientists and analysts can use Dataiku DSS to discover the conditions needed for content campaigns marketed to highly-specific segments… such as an SEO-enhanced blog product review for social media distribution to females, aged 20-35, residing in Austria and Germany. Content analysis can be performed on any medium, such as Web pages, articles, database records, or search engine queries.