Most people in digital are now au fait with the idea of attribution modelling, if not the reality, but in the year of ‘big data’, how many of us are really crunching the numbers to identify the key periods in which to invest?
I’m not talking about knowing when the peaks are and throwing money at them. I’m talking about really understanding when you need to invest in order to assist that peak before it’s even happened.
Think lead times. Think objectives. Think overall channel attribution.
If your objective is to boost limited time price reductions, otherwise known in retail as a ‘sale’, then of course you need to upweight your budget and marketing efforts accordingly. Your objective is to maximise traffic and shift as much stock as possible, for a ‘limited time’ as the humpteenth promotional email says.
But as we all know, you’ll be spending more to give away greater margin, and CFOs don’t tend to like that very much. It’s not great for your brand or unsubscribe rates either.
I’m talking about understanding your customers’ mindset, their decision process, and the correct media to influence that. And of course, the average time it might take them actually result in a conversion.
I know what you are thinking, ‘this is nothing new’, and you’d be right, but I’m amazed how many businesses I meet are still failing to educate the board through their online investment strategies. And with 71% of businesses increasing their digital budgets this year, getting their sign-off is crucial.
So whilst not new, it’s good practise to remind ourselves, and with the new financial year creeping up on most of us, what better time to dazzle the board with well-thought intelligent requests for budget?
Short of getting the board drunk at the Christmas party and writing yourself a blank cheque, in my experience the only way to get their buy-in is through those lovely lovely numbers we have a wealth of at our fingertips.
Time for modelling.
I’m calling this theory ‘intelligent levers’ and it’s all about when and what to pull in order to cause an effect at the right time.
I have split these intelligent levers out as:
- Lead time – what is the average time from first touch to conversion?
- Channel influence objectives – how does the average lead time differ by channel and dynamic, and what are the objectives for each?
- Seasonal influencer - do average lead time and channel influence vary by seasonality?
It’s more than just thinking about one factor – we need to consider them in combination.
To give an example, Luxury Travel (and this is purely an example):
- If my average time from ‘look to book’ is 90-days and I want to to maximise my bookings during the January peak, on a flat average, I would need to upweight my online budget in October/November.
- But then I realise that for sales I make in January, in fact my average lead time for PPC brand is only 30-days, affiliates is 60-days, and PPC generics is 90-days and display is 120-days. So I start to build my budgeting model to take upweight around these dynamics relative to my target peak.
- To add another layer of complexity, this can all be subject to change by seasonality too… In summer months, my PPC brand lead time may only be 14-days, affiliates 40-days, and PPC generics and display both 60-days. So whilst this may not be relevant to my January peak, I need to think about this for my Q3 campaigns.
In summary, my recommendation would be to invest some serious time and energy into understanding how and when each element of your marketing mix can be best optimised in relation to your objectives, and how those elements work together to help achieve your targets.
In the short term, you may suffer spreadsheet blindness.
In the longer term, you’ll present a tight proposal for additional budget that may just get the board’s approval first time.