Before you dive into this article be sure to check our previous post on the first five points here.
The R-cubed Data Scientists like to work by the motto, if it’s easy and it works, use it. With so many of your customers using digital, e-commerce and social media it’s important to ensure that you get their attention amongst the noise, interact and retain them post purchase. We outline how below.
6. Spend more on new customers and new prospects
• When you are new to people, they are more interested: the ROI of first-time contact is always dramatically higher.
• Marketers talk about the ‘afterglow’ – that rather agreeable feeling when we have just bought something.
• That is when customers are at their most receptive, but they cool quickly.
• As customers, they become bored or dissatisfied. As prospects, they will be
ruthlessly courted by others.
• ‘New’ gives us a clean slate for communications. Some marketers ring fence and
protect their new customers – they’re mad.
As we’ve pointed out already: when they’re new they are far more hot to trot. Neglect this opportunity and you throw away a fortune.
7. Ask your best enquirers and lapsers to come back
• ‘Once they’ve dropped off, it’s not worth trying.’ False.
• They may leave or not convert for temporary reasons. We may be still relevant, even loved, but it’s not the right time.
• There is usually data to differentiate the best prospects among enquirers and
lapsers from the worst. These are the ones to re-solicit.
• Remember, if you don’t ask, you don’t get.
We have talked elsewhere about the extraordinary impact and memorability of a simple ‘thank you’. Customers love to be remembered and acknowledged.
8. Sell when your customer is ready to buy
• People buy when it suits them – not you.
• Who would you target? The customers with the right profile, or the ones ready to buy?
• When is more powerful than Who.
• 80 per cent of non-response is down to the wrong time.
• Work out when they buy, don’t imagine your messages will change them.
• Look for ‘hot data’ that could trigger activity.
• An unexpected contact may be a buying signal – say an enquiry about something
you haven’t offered, an insurance claim, or a change of address.
• Make sure staff are listening! This means having ears on all digital channels. You can’t ignore the power of digital and social media, that’s why R-cubed have R-Bot.
Our Data Scientists are particularly keen on this. When the right time is glimpsed, ROI can be multiplied. We are entering a new era when right time means right now, and unlike the generations of marketers before us – we have the technology to deliver against the promise – right message, right person, right now!
9. Keep and use your contact history with individuals
• What’s it worth spending on an individual or a household?
• You can’t say if you don’t know how much you spent in the past – and what it
produced.
• One big sin is comparing customers by past sales, without looking at past
investment.
• If you know what you spent on each person, and what it produced, how do you
use that knowledge?
• You may find that repeat purchasers have had masses spent on them.
• Does it affect future investment? When do you cut off spending?
• Do you use that knowledge by segment, or by individual?
• Do you use mailings, phone, e-mail, friend get a friend, inbound and outbound?
If so what are the key moments when it pays off? Is data recorded? Is it usable?
At R-cubed we found that smart use of contact history can outperform all other data. But, we lament, some clients still look at buying third-party data first.
10. Use silent controls to prove real incremental impact
• ‘The mailing did well – 20 per cent response!’ ‘Yes, but how many would have bought anyway?’
• The real response cannot be known without a controlled test. No technology or technique can overcome this.
• This is a basic rule of targeting – it’s worth many millions of pounds.
• Yet amazingly, few companies measure real results.
• No measurement – no comment.
Our Data Scientists believe good marketing generates incremental sales, not ones you would have got anyway. And for that you must follows the basic rule of testing: compared with what? He elaborates on this in his last point.
11. Ruthlessly keep demanding ‘why did they do that?’
• You’ve had a good idea – how will you know it works?
• Test it against doing something different, or nothing at all.
• A properly tested result = learning = repeatability = gains.
• This is the beauty of DM – it is easily measurable – but you have to want to learn.
• Untested activity implies a ‘we can’t improve it’ attitude, but there is always a
better way to get more ROI.
• But poor (or, even worse, no) testing = wrong insight = waste and loss.
• And good analysis is driven by good questions, not data or statistics.
To give you an idea of what this sort of thing means when it comes to money, we asked chief Data Scientist Kate William: Take the largest direct marketing category – financial services. Suppose you start with something pretty basic. Say you analyse your customer base, looking at things we’ve already mentioned like recency, frequency of buying and how much they have spent as a result of other offers. This alone could increase your sales by 25-30 per cent, and your profits by 200-250 per cent, since you make these extra sales with no extra budget. This very simple analysis is not advanced metaphysics – it’s been around for at least 50 years. Yet an astounding number of allegedly sophisticated firms don’t apply it.
Then suppose you do a test mailing and look at the response and conversion results so as to target better. Again, not magic: just good sense.
This could increase your sales by 70-75 per cent and your profits by 400-450 per cent since, once again, you make these extra sales without spending any more money.
If firms applied themselves more to such analysis than frittering away time, money and executive energy on navel-gazing about their new slogan, mission statement or sexy TV commercial they would post infinitely better profits.
Based upon the original text from Drayton Bird’s book.
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