Using data science for engagement marketing
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This document discusses engagement marketing strategies using data science. It notes that a huge amount of digital content is created every minute across various channels like YouTube, websites, emails and social media. It emphasizes thinking about customer touchpoints across channels and using both fast and slow data to better understand customers to tailor personalized messaging. Some key takeaways are to start engagement scoring, think about existing customer knowledge, use data to improve understanding, communicate carefully and start engagement efforts slowly.
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Using data science for engagement marketing
- 1. Engagement marketing: Using data science to go further
- 2. 72hrs of you tube video 571 new websites 100m new emails 277,000 tweets .. created every minute
- 3. The age of context
- 6. What should you think about?
- 7. What do you know?
- 8. Fast data versus slow data
- 11. What we speak about becomes the house we live in - Hafiz
- 12. › Think about the touch points with your customers across the various channels › Start with what you know › Use slow data to better understand your customers to then tailor more messaging to more customers › Do start slow › Be careful about how your communicate Key takeaways
- 13. Thank you
Notes de l'éditeur
- Engagement marketing requires a greater understanding of your customer. It relies on two aspects to be successful – content and context. Without both your efforts to engages will fall flat.
- We have all heard the phrase Content is king Lots of content being created Need to cut through the noise Cant rely on getting the perfect content that jives.. So with all this content being created..
- .. its irrelevant if it is not delivered in the right way. Marketers need to understand the what, why, where and how of their customers to ensure the content is received and understood. Its not pleasant eating a pizza out of the gutter ;)
- The challenge of course, is alongside the growth in content has been a growth in the number of channels and with it the amount of data that a marketer has to deal with. Consumers though don’t distinguish between channels. They don’t think about the email channel or the mobile channel. They expect everything to be joined up. They expect you to understand what went on before and that it makes sense.
- So how do you get a clear picture? To deliver on context you need to understand the data being sent back by your customers. As Ray said – it is about having a 2 way conversation.. The first step is not to worry about the amount of data being generated. A forrest research report found that around 45% of marketers were hamstrung just worryign about the pile of data they had. But the trick is to take it one step at a time.
- First Think about the different phases your customers might go through And don’t stop at the sale – look beyond. The sale is merely the beginning of the relationship
- For each of those what might work at each of the touchpoints – whether that is in store, over the mobile or via social. Before you do any heavy integrations test the touchpoint messaging and see the response! Whether that is a 1-1 message via social or a triggered message via email or mobile apps.
- So that sgets you started and most of the hype spends time on what you can do in realtime. If someone does X then do that. don’t forget slow data. There are patterns in your data which allude to behaviours of your customers that you may not be aware of. There may even be phases that arent immediately obvious. Mobile phone example? Its not about generating lots of noise for the sake of it. But frequency does help – if the messaging is right.
- Used patterns in the data to identify about 25 products that assigned each shopper a score. could also estimate a due date to within a small window so Target could send coupons timed to very specific stages of her pregnancy.
- Another case study, this time looking at attributing a score to each of your recipients. This is obviously custom to each company based on whats important. Everyon’s list is different – did you do several competitions a few years ago to help build your list? Their motivations are going to be very different to those who have been buying regularly from you. Don’t forget timing can play a part here – someone might be disengaged due to their point in the lifecycle and not just because of their previous reasons for interacting. For example the extreme example is an insurance company – you really don’t care about which insurance company to use until the 3 day run up to renewal so how do you keep them engaged.. Once you understand this, tailoring your messaging accordingly can deliver a significant increase in performance – a project recently delivered a 133% uplift in click rate.
- With all this comes a level of responsibility though. You do need to be clear about how you are using the data. Regardless people don’t want to feel spied on so copy is important. Target example – position another non related product alongside the related one. Timing.
- Think about the touchpoints with your customers across the various channels. Don’t just think about if this than that type messaging Use slow data to better understand your customers to then tailor more messaging to more customers. Do start slow. Be careful about how your communicate.