Wouldn’t you love to know the future? If you are a marketer, such knowledge would mean designing campaigns that always have 100% conversion rates, always knowing the best offer for customer retention, achieving astronomical ROIs, and landing that dream job at Google, Amazon, or Dunn Solutions. Sadly, unless you have a crystal ball, the best you can do is to look at the past and see if there are any patterns that you can use to make educated guesses about the future. Marketers have realized this early on and historically have employed the aid of reports and dashboards to amp up their understanding of market trend and consumer behavior.
But the digital revolution we have witnessed in the last few years has created new and much more complex challenges for marketers. The explosion in the number of touchpoints has made the customer journey a maze for which standard marketing tools are not adequate. Furthermore, consumers’ expectations have skyrocketed. They expect offer personalization and content, and personalized customer service If you are a marketer and still digging through charts and tables of historical information, let me be blunt, you are wasting your time trying to make sense of that chart where number of shark attacks increases along with the amount of ice cream sales in summer!.
I am sure that you have the skills and expertise to analyze a few data points and draw meaningful insights from them. But can you do it for millions of data points? Can you do it error free? Can you do it in near real-time? Can you tweak just one variable, repeat the process thousands of times, and select only the best outcome? All this by continuously incorporating feedback and improving on it? Would you even want to do all this?? (Impossible and...boring!!!)
So why not offload this tedious and impractical worke to a machine that can do all that stuff for you so that you can focus instead on the value-add side of marketing, such as planning and content creation? Machine learning has left no area of marketing untouched. I could list numerous specific tasks that marketers could use ML for but for the sake of simplicity I will group them into three main categories:
- augmenting workers
- process automation
- process optimization
Machine Learning supplements the human brain and does not replace it. Marketers are often reluctant to employ Machine Learning because they often see it as a replacement for human expertise; This is far from the truth, and the organizations that have strongly believed and invested in the co-existence of human and artficial intelligence , have experienced unprecedented growth (Facebook, Yelp, Apple, Amazon, Google, Pinterest, Twitter, IBM . . . just to name a few). Machines cannot think out of the box (no pun intended!) and will not replace creativity, empathy, the ability to innovate, or common sense. However, computer based algorithms are infinitely faster and more accurate in processing data and return fact-based solutions to well-defined contexts. Therefore, marketers focus on creativity way while using machines to augment human output and get a lot more out of their efforts!
There are inefficiencies across an organization that can be improved by machine learning. We’ll continue to focus on marketing. Historically organizations have used technology to cut costs in labor-intensive, yet static, marketing processes. A perfect example are marketing automation tools that enable marketers to execute their email campaigns efficiently; basically making it easier to send lots of
emails. However, the business world we live today in is not static; the unprecedented speed with which consumers can compare offers in real time and the hyped focus on personalization require automation services that quickly adapt and immediately enhance the consumer experience. Netflix provides an example of Machine Learning Automation to improve the viewers’ streaming experience; when the user logs in, several movies are recommended based on historical data; and as the user interacts with the platform (surfing pages, reading movie descriptions, etc.) several actions are triggered automatically; the user will see more recommendations, and based on his/her action, the process is adjusted again.
Machine Learning brings velocity to process automation. Even if we had all the insights needed to take the correct action for a given situation, would we be able to take the optimal action (where “optimal” means one that maximizes the business goal)? Machine Learning does exactly that. A perfect example is the dynamic pricing algorithm designed by Uber. Uber will take into account several factors (such as the presence of an event, number of available drivers, day of the week, etc.) to calculate demand for the number of rides. When demand is forecasted to be very high, the price is adjusted up to optimize profit and weed out people willing to find alternate means of transportation thus maintaining relatively short wait times.
Given these examples, you can see why Machine Learning and Marketing are a match made to last. Each builds on the strength of the other, and coexistence is not just possible but desirable. Dunn Solutions brings velocity by: generating quick and actionable fact-based solutions; marketing automation; and process optimization within the marketing platform. At Dunn Solutions, we have been very aware of this; in fact, we even have a name for this process: we call it, the velocity virtuous cycle of customer engagement. Dunn Solutions’ expertise has helped many organizations improve their marketing ROI by assisting in any, and all, of these areas. Contact Dunn Solutions today to learn we can bring velocity to your organization's marketing initiatives.