For our client, an international start-up company (South Africa, Great Britain, Switzerland…), we are currently looking for (1) behavioral data scientist and (2) client delivery analyst.
Table mountain, Cape Town (SA)
The client you would be working for is a company who provides big corporations with employee behavioral analytics. Our team is responsible for building and maintaining their analytical platform as well as for supporting the internal team of behavioral scientist in developing measurements.
The positions we are offering are demanding but do come with their unique advantages. Firstly, we don’t mind when or where you work as long as you deliver what you are supposed to. Secondly, you will have a huge opportunity to grow in data science and related fields, supported by our experienced team. And thirdly, you will be in direct contact with international start-up environment. Continue reading
In the last few years the obvious fact that for successful marketing you need to “contact the right customers with the right offer through the right channel at the right time” has become something of a mantra. While there is nothing to disagree here, it is a pity that for most part the saying stays in words and only gets realized in rare cases. The issue is that while many can repeat the mantra, only few actually know what is needed to put it in practice. In this post, I am going to talk about the first part – how to target the right customers for your marketing actions?
There are many approaches to solving this great puzzle. One of the extreme solutions is having a team of marketing experts who rely solely on their gut feeling, projecting their opinions on customers, without any proof, not even evaluating or testing the campaigns. Because that’s what they did in their previous job. It might sound ridiculous in today’s digital era, but surprisingly it is often the case.
The other extreme is building complex AI engines and let them make all the decisions. This is typically a proposition by some geeky start-up run by fresh PhD holders. This approach is in my opinion also wrong. First, you have absolutely no assurance that the data available truly reflect the reality, that the algorithm works flawlessly or simply that the randomness in the world is not too strong to predict. After all, even companies running algorithmic trading have human dealers overseeing their algorithms, who focus on addressing weaknesses of the algorithms and generally on preventing internal disasters.
As always, I think that the solution lies somewhere in between. An experienced marketer, whose opinion is backed by information extracted from the data available, can truly hit it. Imagine that you have to run a campaign to increase sales of a saving account (or a road bike, new robot, a holiday in Caribbean…). The long proven data extraction technique one should consider is called propensity to buy (or to purchase or to use).
Driving marketing budget sometimes seems to be a mysterious art where decisions are based on ideas of few enlightened people, who know what’s right. But you should not fool yourself, the times are changing and so is the way successful marketing is managed. The same as in other fields, experienced marketing managers use information hidden in data to help them. With the amount of data and methods available, it is however often tricky not to get lost and be able to distinguish the signal from the noise. Typical examples are the marketing attribution models – a tool that is widely used, but in my experience rarely maximizes the leveraged value of data.
Typically, in marketing attribution, marketers want to know, which part of the business KPI (typically site visits, sales, new customers, new revenues etc.) result from which marketing activity. Mainstream approach is to use attribution models that are often very simplistic – like single source attribution (last click, first click) or fractional attribution (where the contribution is distributed among multiple touch points given some simple rule). These methods provide marketers with the importance of each marketing channel or campaign in respect to their KPI. Based on this historical information the marketing managers make a decision about how to allocate the marketing budget. This approach however puts a great deal of pressure to tedious and demanding data detective work to make sure all client touch points are measured correctly. More importantly, there is no way of knowing that this work has been done correctly, which of course has significant impact on credibility of the attribution models.
Knowing these difficulties, we decided for an alternative approach. We thought: Why should we dig into the individual touch points? Shouldn’t we rather focus on marketing investments and model the ultimate business output? And that is exactly what we did. We took investments into individual marketing channels in time and used time series analysis to predict our client’s business goal (number of sales). On top of it, we also added seasonality, marketing investment of competitors and some other simple parameters.
“Even though we are using data to drive marketing decisions on a daily basis, most of the tools that we have used up until now focus on describing the past. Recently we decided to work together with aLook Analytics to change that. Thanks to their modelling approach to marketing investments we now have accurate information about the expected future developments as well.
Using the interactive Shiny application that is built in Keboola Connection, we want to make informed decisions on the fly, which will help us to reach our sales goals in the most cost efficient way.”
Daniel Gorol, BNP Paribas Personal Finance SA / Cetelem
In the final post about practicing data science in SMBs, I will get into more details about how exactly a data science project looks like, what are the essential phases and last but not least how to maintain the deployed solutions.
Adam has recently had a chance to talk to Pavel Bulowski from Keboola Asia as a part of their Data Cofee Talk blog series about life and work as a Data Scientist. You can find the whole interview here.
In this part of the series I would like to explain what does “data science as a service” mean. What are specific examples of data science solutions? What should you know when you decide you want to give it a try? What will typically be the requirements on you and your internal team?
The post is especially aimed at applications of Data Science in e-shop environment.
The benefits of tailor-made solutions
The great thing about purchasing “data science as a service” is that the service can and should be tailor-made, designed to suit the exact needs of your business. This is important for two main reasons. Firstly, it gives you an edge over competition (who does not have it) and secondly, because you only pay for features that you will use.
Data science or its alter egos – predictive analytics, machine learning or AI – can, if implemented correctly, offer an impressive ROI. Moreover, together with new technologies and products being developed, the data-driven approach becomes more accessible every day. Even medium and small businesses can now leverage the value of data science. In our experience, the proposition is even more rewarding for them than for large corporations.
Why is that? What are some typical applications of data science for small companies?
Watch our Brighttalk webinar to hear more.