Could you be more visual, better optimised & less tech-led?
Within this month’s theme of database marketing, we also want to consider related customer insight developments. These include ways other insight skills can help. For example with problems like getting your multi-channel interactions optimised.
As we’ve shared before, Holistic Customer Insight is much more powerful. It benefits from the synergy of using data, analytics, research & database marketing together. Previous posts have focussed on how this approach helps proposition development or marketing measurement, but database marketing can also benefit.
Understanding from research the ‘felt experience’ of your customers in response to targeted comms or inbound prompts, can help improve their design. Improved analytics & data visualisation can help both improve targeting & spot unintended consequences of optimisation.
So, for this post, I’m returning to the world of reviewing other data, analytics & research related blogs. Sharing videos or articles I think should be of interest, to those leading database marketing or insight teams.
How is data visualisation developing?
I suspect most of you have heard of IBM Watson Analytics, the research & products from IBM under the brand of their Machine Learning champion. What IBM chooses to call a ‘data discovery service’.
It’s encouraging to see the team there now engaging with wider topics of interest to those working with analytics (beyond just more advanced AI or modelling algorithms). Their ‘Expert Series’ of YouTube videos is worth watching. They are short & include a good range of different analytics experts & pundits.
A recent episode is this video focussed on trends in Data Visualisation. Good to see Randy Krum share from his real experience in this area. Given the need for database marketers to engage with new ways to better understand complex multi-channel performance data, these developments should be very relevant. Well designed interactive data visualisations surely have the potential to transform dull & sometimes obscure campaign reporting:
Can Machine Learning deliver the optimisation you need?
An even hotter topic than data visualisation, as you’ll have seen on social media, is Machine Learning. It seems like almost every vendor now feels the need to offer something in this space of express an opinion.
There are also a lot of useful resources out there, including those I’ve shared on Twitter & Google+ (e.g. courses & resources from Jason Brownlee)
Of particular relevance to Database Marketers, is the potential for Machine Learning (AI) algorithms to learn how to best optimise leads. Lots of bloggers will talk a good theory in this regard, but this very helpful tweet from Per Harald Borgen shares how he did it. He includes APIs & scripts used to prepare data and develop the model. Well worth a look if you’re tackling multi-channel leads & how they can be optimised:
Boosting Sales With Machine Learning: How we use natural language processing to qualify leads https://t.co/yaAuQqstaq
— DEV Community 👩💻👨💻 (@ThePracticalDev) June 7, 2016
Are brands being damaged by tech-led customer research?
Brand research can sometimes feel like the other end of the spectrum of customer insight skills, from the commercially minded database marketers. Pragmatic DBM analysts can view such research as somewhat fluffy or high-level and perhaps not of relevance to their work.
However, a clear understanding of how a brand is perceived & impact of interactions & campaigns on brand sentiment can be key to improving that impact. Without an appropriate programme of research, database marketers risk being blind to the emotional impact & recall being prompted in customers by their messages.
Many organisations have been keen to benefit from technology developments in research (inc. survey) execution. This is often because of the promise of reduced costs & faster delivery. However, this post from Jeri Smith sounds an important alarm as to the risks of being led by such technology options. Worth a read & then a discussion with your research peers as to the appropriateness of your current research programme (to complement & inform DBM campaigns):
What helps you?
I hope those “shares” were of interest. Even if they simply prompt a wider look across the customer insight skill set. I’d advise that, when you’re considering how your database marketing programme could be better optimised.
Have you found other developments that helped you better target, execute or measure the impact of your database marketing? We’d love to hear what works for you.