Organizations such as Amazon have shown us all that implementing personalization correctly, while complex, shows superior conversions and superior ROI. Last year we saw several important organizations start to position themselves for better personalization.
Here are some of our favorite personalization implementations and announcements from 2013.
- Leading CMS providers such as Sitecore and Adobe introduce amazing new out of the box personalization features.
- Netflix added awesome new personalization tools to its streaming service.
- Google acquired data learning organization DeepMind to better understand complex user behaviors.
- iTunes launched new personalization recommendations features across movies, shows, songs, and books.
- Forward thinking organizations like Klipboard and Pandora continued to improve the personalization algorithms (and added new users as a result.)
- Google started to marry its Ad Network demographic data with Google Analytics. While this doesn't necessary drive personalization yet, this has massive potential.
- My favorite personalization announcement was in August when Marissa Mayer proclaimed in an interview that Yahoo is "really a personalization company."
If 2013 was the year of BIG data, then 2014 will be the year of how we use that data and in turn, utilize superior personalization algorithms. Most organizations now have access to new data sources and new tools to implement personalization features.
Below is a partial list of personalization types that we expect to see grow quickly in 2014.
Enterprise Personalization: Over the last decade, personalization has been mostly department specific (and even siloed within departments). Digital marketing had their personalized websites. Demand generation had their personalized emails. The personalization algorithms were never connected. Lead nurturing platforms and advanced email platforms have started to enable marketers to have integrated personalization that spanned 2-3 channels. In 2014 and moving forward we can expect to start seeing enterprise based personalization strategies that begin with a core data platform (i.e. CRM) and then spread to all relevant departments (i.e. call center, event marketing, in store, email, social, sales, etc.).
CRM-Based Personalization: Most digital personalization to date has siloed. Moving forward we will always be looking to drive personalization across multiple back end systems. Coordinating CRM data with web analytics data, mobile application data, and other digital intelligence allows the website to offer personalized content as well as empowering sales teams to personalize discussions offline.
Call Center-Based Personalization: Over the last several years several organizations have shown that many prospects and customers seeking support will utilize both the digital channel and phone support. Both of these support mechanism house unique data on customer support behaviors and needs. Integrating the two provides benefits for both channels to decrease the amount of time in between a customer request and a customer solution.
Ad Network Demographics and Re-marketing Data, Integrated with Web Analytics: As mentioned before, Google started to marry the data of their Ad Network with Google Analytics. This data, with the addition of click through data from re-marketing efforts, offers a treasure trove of data. Combining this data provides great opportunity for delivering highly personalized content, imagery, and navigation.
Behavioral-Based Personalization: Very few organizations have successful implemented behavioral-based personalization yet. It is one of the most complex personalization algorithms and possibly the type with the most false positives. This is partially because users change their behaviors rapidly. For example, when I am looking for a good documentary to download I am a hunter often looking for a specific title, but when I am looking at Sci-Fi movies I am a browser trying to find something new. This type of personalization will start to be more prevalent in 2014 (and 2015) because organizations are starting to have better tools for tracking, identifying, and exposing behavioral data. Companies such as Sitecore and Marketo are starting to identify multiple behaviors for each user, then exposing that information to personalization engines.
IP Address: Advanced web teams have been doing IP-based personalization for almost two decades. But the available data for IP-based algorithms is starting to get really cool. Organizations such as Demandbase, Acxiom, and Maxmind are using IP addresses to provide websites with awesome information about visitors such as their forecasted weather, their currency, the affluence of the neighborhood they live, the most common illnesses in their area, the number of offices their employer has, how spread out the offices are, number of employees, revenues, profits, etc. With this detailed information, both B2B and B2C organizations can drive highly advanced personalization of content, imagery, and site functionality.
Collective Networks: Ad networks are impressive in their ability to estimate visitor demographics based on the sites a visitor views. These ad networks are getting more accurate everyday with common attributes such as age, gender, and interests. But ad networks usually only share this information with programming for display ads, emails, and landing pages. We are starting to see organizations such as Quantcast provide site owners with glimpses into this data set to drive onsite personalization independent of the ad network.
Mobile Personalization: Personalization has been very slow to appear in the mobile website world. It's common in mobile apps, but rare in mobile sites. There is good reason for this. Mobile sites are highly scrutinized for load times and personalization can add a lot of server calls slowing a page load. But more and more users are switching to faster phones on faster networks. With more bandwidth and more processing power comes the ability to put more functionality in pages and personalization functionality often has the highest ROI.
To date many organizations have not yet implemented powerful personalization features due to the lack of data and the relatively high costs associated with these types of features. But in the last couple of years we have seen more and more organizations providing the necessary data at little to no cost. We’ve also seen platform providers creating developer friendly tools to incorporate this data to drive personalization. The improved business results derived from personalization are well publicized and fairly consistent amongst the organizations that implement it. Within the next year personalization features will move from being the strategy of the technologically elite to the table stacks feature set of leading customer service and leading sales organizations.