When faced with marketing in the data-rich digital world, we are tempted to go in many directions at once. We want our marketing campaign to be data-driven and as automated as possible. But the challenges are immediately apparent when we consider the variety and velocity (as well as the volume) of data that are doing the driving. For example, we have data from multiple sources (internal and external to your organization’s servers), from multiple channels (web logs, purchase transactions, customer service interactions, customer reviews & comments, social media, hashtags that call out your business products), and from multiple viewpoints (context, content, sentiment, time, and location).
Therefore when faced with this complexity, it is good to follow the K.I.S.S. principle. No, not the usual KISS principle, but this new one: Keep It Simple and Smart!
Simple – Start with your key business objective for each campaign and identify the essential steps that will get you there. Link each step in the customer engagement journey (awareness, acquisition, conversion, and retention) to a set of measured inputs (customer metrics) and desired outputs (a customer persona linked to a specific campaign). For example, if you aim to increase customer awareness of your products, then social media promotions may be the best approach. If your web analytics identify potential customers coming from particular social media platforms, then promote your product in that space (e.g., via Facebook likes or Twitter #hashtags). If you aim to maximize customer retention, offer your customer an incentive to stay engaged, either through an occasional discount offer or some form of gamification experience that keeps them within your online property. The customer-marketing linkage should be kept as simple as possible, but no simpler (to paraphrase Einstein, who said “Models should be made as simple as possible, but not simpler.”)
Smart – For each marketing opportunity, remember the analytics! The analytics include the data science-validated customer features (input metrics), algorithms, and responses that optimize the desired business outcomes. This is essentially the same as a standard functional dependency R=f([x]), where the response R is determined by the algorithmic function f and its set of inputs [x]. Syntasa’s platform is autonomous digital marketing through data science – first, we learn f([x]), and then we recommend the appropriate response action, R. It can be automated and thus be immediately responsive to dynamic changes in the customer’s “persona” (context, behavior, location, time, interests, and/or intent). The right call-to-action can be targeted to the right customer at the right time in the right place. The knowledge required to know what is the correct response, and when to deliver it, is acquired through advanced machine learning and data science (including A/B testing of various scenarios).
Some of the statistics and benefits associated with businesses who use marketing automation include:
- CMOs indicate increased revenue (79%) and higher quality leads (76%) after implementing marketing automation.
- B2B marketers who implement marketing automation see a 10% increase in sales-pipeline contribution.
- Organizations using marketing automation see 53% higher marketing qualified lead (MQL) conversion rates and 3.1% greater annualized revenue growth rate than non-users.
- Organizations using marketing automation indicated better insight into multi-channel attribution (36%), compared to companies with no automation (11%).
- Marketing automation drives a 14.5% increase in sales productivity and a 12.2% reduction in marketing overhead.
Subscribe to the K.I.S.S. principle in marketing automation – it is simply the smart thing to do.
For more from Kirk Borne, follow him on Twitter: @kirkdborne
Blog post originally published: December 15, 2014
Kirk Borne
Advisor to Syntasa | Principal Data Scientist, Executive Advisor, First Data Science Fellow at Booz Allen Hamilton
Dr. Kirk Borne is the Principal Data Scientist, an Executive Advisor, and the first Data Science Fellow at the global technology and consulting firm Booz Allen Hamilton. Before that, Kirk was professor of astrophysics and computational science at George Mason University for 12 years, specifically in the data science program. Prior to that, he spent nearly 20 years supporting data systems activities for NASA space science missions. Kirk has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. He is an elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. Since 2013, he has been listed consistently each year as a top worldwide influencer in Big Data and Data Science on social media.
