This will be a short post outlining my thoughts on trying different approaches to solving problems (mainly in the context of data analysis, but the ideas might extend beyond that). My main conclusion is this: always try the simplest thing you can think of first.

Imagine the situation: you’re starting a new project and you’ve been assigned a goal, predict which product(s) to recommend to a customer when they come in to your shop/visit your website. Adding some detail, imagine that you work for a company that sells day to day essentials, food, etc. (i.e. a supermarket). You track what customers have bought in the past (maybe through an app they use if they’re in the shop) and those data are available to you.

Sounds exciting, right? it’s going to be like Netflix - you can look at what people have bought, try and find similar people (based on their purchase history), and recommend products that way. You’ll be just like Netflix (only you’ll recommend different brands of milk rather than genre-bending interactive media experiences). But wait, have you tried the most simple thing you can do first? To (mis)use an IT support phrase - have you turned it off and on again?

What might that simple thing be, though? Well, here are a few very simple ideas:

1. Count all sales, and find your number one product across all customers; recommend that to everyone.
2. Find out each customer’s most bought product; recommend that to them.
3. Combine the first two approaches, so all customers get both a general and a personal recommendation.

But surely this won’t work? It’ll be too broad, and the recommendations will be useless! That may be the case, but you need to test that before you move on to more complicated methods. Of course you’ll need a reasonable framework to evaluate the success of your simple method (which is outside the scope of this post), but you should test and evaluate it nevertheless.

Until you’ve tried you don’t know that it won’t work. It might actually turn out to work quite well (in this example, it would probably do “ok” for some, frequently bought, items); maybe you can save yourself the time and effort doing something more complicated. And that’s what trying simple things first lets you do: justify the cost of something more complicated once you know that the simple answer isn’t the right one.

That last point is really my main conclusion and motivation for this post. A complex method is never justified until a simple one has been tried first. In our example, yes, you could build a much more complex recommendation system, but unless you’d tried and evaluated some sort of “count and rank” method, maybe a frequency analysis (e.g. how often does a customer purchase vs. when did they last purchase), or a simple-ish market basket analysis (i.e. association rule learning) I don’t think I’d be sold on a more complex method being the best answer. And I certainly wouldn’t be convinced that the time and/or money you spent developing it was justified without seeing some results that showed a simpler method just didn’t cut it.

Try something simple first, it might work, and if it doesn’t you’ve at least got a reason to try something more sophisticated for round two.