Tim Harrison
speaker
197 appearances
2 recordings
1 series
first heard Jun 2022
last heard May 2023
Tim Harrison’s voice in public audio — every appearance, attributed to the second.
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the things you look out for to identify whether that culture's ready and and sort of any other ideas you have for how to get there for data teams that want to start doing this, but maybe they're not sure if their organization's uh there yet.
That's that's extremely interesting.
And there's loads I want to unpick there.
But I guess thinking on um one sort of word you you mentioned there was sort of the driving value and measuring sort of the ROI and the impacts and the uh
thinking about kinds of
projects that we often see things about sort of churn prediction, where can we upsell prediction, cross-sales prediction and propensity modeling, they're finding the best opportunities with our customers.
what do you see as the best practice there and how to demonstrate that value to the business, either sort of up front at the beginning of the project?
Does do you sort of recommend trying to make that ROI case early on?
Or do you say, well, actually we've got to do
A six week sprint, get a proof of concept and then demonstrate value?
Or how do you normally see that working for for your customers?
I think you we see both.
My personal preference
And I guess there's a there's a big step between sort of building your model, whether that's machine learning or just sort of
Some feature correlation of what you're seeing in the customer behavior data is correlated or predictive of likelihood of a churn or likelihood of a cross sale if we're one of those use cases.
There's a big step going from that and having a model that can help predict that to actually operationalizing it and then driving either a reduction in turn or
uh new cross-sales opportunities.
How how do you see that connection between, I guess, what is the probably the data team or the analytics team driving that initial modeling and analysis to the the business teams?
A really interesting message, and I think it's one that that we see as well in the HD data team, which is often there are cases where we do machine learning models and that can be highly predictive and highly powerful and really drive impact.
But often, especially when maybe there hasn't been so much before, some of that simple analytics and looking at a few key drivers can be really a quick win there.
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