Marketing in a Machine Learning World: Simplicity is the key!
Perhaps, it is one of the most exciting times to be marketing in this digital world because marketing is increasingly becoming more high profile job than ever in most of the organizations! But that also means that marketer’s life has become more complicated despite having more tools at her disposal. The reality is that the marketers have to worry more about problems like how to break through the noise in this hyper competitive era; how to drive loyalty; shift from product-centric to customer-centric model; deal with the saturation in social media – everyone is a content producer nowadays; changing demographics; understanding customer’s context; how to justify ROI internally; having consensus for creative assets; the confusion around attribution model and spend; the changing relationship between brands and consumers; keeping pace with technology; sentiment analysis; omni-channel customer journeys; rise of mobile; figuring out millennial; why so many shoppers are dropping out of their marketing funnel; lack of alignment with sales; lack of trust in the customer database and so on. The list is very long and it will continue to evolve. In the marketing world, what needs to be done is usually clear. But what isn’t always clear is how to do it in an optimized way.
Is there anything different Marketers can do to deal with these problems?
So, Why is everyone talking about Machine Learning Now? Machine Learning (ML), a branch of artificial intelligence, in simple terms focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine learning is almost like an intelligent assistant that draws from fields like Artificial intelligence, Statistics, data mining and optimization.
- Big Data – You have more data than ever. Machine Learning becomes better and relevant with more data. A lot has been written about big data so I won’t go into too much detail.
- Affordability - Till few years back, the machine learning technology wasn’t accessible easily to marketers – the cost to setup infrastructure and build specialized team was just very high. In the past, successful use of machine learning algorithms required made-to-order algorithms and huge R&D budgets, but all that is changing. IBM Watson, Microsoft Azure, Google and Amazon have launched turnkey cloud-based machine learning solutions. At the same time startups like Idibon, MetaMind, Dato and MonkeyLearn have built machine learning products that companies can take advantage of. I have used some of these libraries myself and found it very simple to use but very powerful. Again, these models aren’t perfect, but they’re very useful.
So, What is happening in Industry with respect to Machine Learning?
Is anybody in your organization looking at all the innovations happening in the machine learning world; doing gap analysis and making recommendations about your machine learning roadmap?
What can Machine Learning really do for a Marketer?
- Finding and predicting best and least valuable customers from lifetime value standpoint
- Building personas based on customer clusters and building appropriate creative, content and services for them
- Recommending new products and content based on who you are which prospects are most likely to buy;
- Tagging content with right keywords
- Testing countless paths consumers may take through content
- Programmatic ad buying
- Optimizing moments of interests by personalization of content
- Predictive lead scoring
There will be many more use cases in your organization if you explore and go deeper.
What is Missing? Why Everyone in my Organization is not using it?
- Democratization, Simplification and Training of Machine Learning concepts - I believe what is lacking is bare minimum machine learning training at conceptual level for marketers – from executives to marketers who are in trenches – so that they can start having the right conversations. The whole concept of using machine learning needs to be simplified and they should know how to make it actionable. Customized machine learning training should be developed for marketing and e-commerce organizations. No, marketers don’t need to become data scientists for that. They just should be able to exploit the machine learning technologies already existing either in their organizations or outside in the cloud. The idea is to develop a primer for them so that they can start having meaningful conversations with data scientists.There are some very good books out there for machine learning but they are either too technical or very high-level to be actionable. Marketers just need to articulate the problem in a better way with some understanding of ML! Defining the machine learning problem precisely is the hardest part and it should be initiated by marketers – not data scientists.
Example of Marketer’s Question
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Machine Learning Algorithm by Data Scientist
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How much should I spend on advertising to achieve certain sales volume?
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Linear Regression
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What are the chances that the customer will a) stay a customer b) re-purchase a product or c) respond to a direct mail?
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Logistic Regression
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Can the machine help me discover segments or clusters in my customer base by using already known factors in my customer base?
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K-means
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How can I match my customers’ interests with the description and attributes of my products?
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Support Vector Means
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How can I predict customer retention and profitability?
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Random Forests
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How can I identify the right audience to target on social platforms and what should I say based on what data tells us?
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Deep Learning/Neural Networks
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How can I develop Amazon-like recommendations on my website?
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Collaborative Filtering
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Of course, you will have to dig deeper and iterate over each of these questions. But asking the right question is the first step to start meaningful conversation with data scientists and technologists in your organization. This will also relieve too much burden from data scientists as they are generally overwhelmed with work and the complexity of their work is never understood properly.
- Agility and workflow of Machine Learning Implementations – You can have a great team of machine learning engineers and data scientists but the process of deploying applications based on machine learning is usually so slow. How do you deploy models in 2 weeks versus 3-6 months? So there is a disconnect between what marketers are being asked to do versus what is produced by data scientists from timing standpoint. It becomes harder if you are dealing with millions of customers and large data. You will probably get two different viewpoints if you ask a marketer versus a data scientist in your organization! Agility in machine learning deployment models will not be exactly like agile software methodologies but there are many common elements. The big difference is that ML agile methodologies will be data driven. To solve that problem you need to customize good agile project management practices and apply them in ML context for your organization. But again you can’t do much about it unless you are trained, as mentioned in point above, in basic concepts. Machine learning in production is becoming less about algorithms and becoming more focused on the data workflows surrounding them. Data Flow is about all the steps required to train machine-learning models in the lab, deploy those models into production, monitor and evaluate their performance, and iteratively improve those models. If your data flows are long, expensive or manual than you have a huge problem. You need to rethink about strategy and consider alternate solutions to simplify it. The last mile problem of machine learning is well known. Most data scientists will not know deeply about marketing or/and marketing systems to embed predictions into the daily routine of marketers.
Source: http://priyankmohan.blogspot.com/2016/11/marketing-in-machine-learning-world.html
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