Marketing is a very numbers-driven function, or so we’d all like it to be.
There is no marketing department that spends less and less every year. And yet, revenue isn’t predictable. Is marketing throughput increasing? That’s questionable too.
Think about this: For most of you, more than 80% of the variable marketing budget goes into discovering which leads are not interested in you. I have assumed some fixed costs like people, branding efforts, etc. that you would have to spend irrespective of the immediate outcome.
Now if your, annual marketing budget is $1M, and if the budget for paid and organic acquisition is $750K, 90% of that $750K ($675K) goes into discovering who is not likely to be a customer. How to improve marketing throughput? How to judiciously gain exposure to the right prospects?
Enter Predictive Marketing!
Typical predictive systems are priced upwards of $75,000 a year. Therein comes the knee-jerk reaction from bean counters in us. But, predictive marketing gets you predictable revenue. Let’s see how.
Let’s take a real customer scenario (with names redacted).
A Truck-load logistics company (in layman’s terms, this means a company that transportation company that ships items that occupy a full truckload) typically goes for deals that get them an annual revenue of, say, $500,000 and it takes 6 months’ lead time to close such deals, at the minimum.If the annual target is $5,000,000, you need 50 customers. Roughly, in traditional marketing terms, this means you need close to 4,000 companies (at 20% win rate for a qualified lead).
The moment, you see a number like 4,000 (qualified) prospects, a marketer knows that he/she needs to build a list of at least 200,000 to start reaching out. (assuming 2% positive response rate, which is very aggressive) This is where ‘spray and pray’ starts.
With Predictive, you don’t have to reach out to 200,000 prospects. Typically, in our experience with a customer for whom we did predictive analytics, leads with a score from 8-10, as qualified by the predictive algorithm, make up for 5% of the list. This is before the sales reps qualify it. At a win rate of 25%, that’s roughly just 200 warm prospects to speak to.
200, how? You need 50 customers. If your win rate is 25%, you need to speak to 200 warm prospects. Assuming a 50% drop off even on warm prospects, you’d need to start with 400.
If 40% of the people you connect with become warm, (Connects to warm leads rate), that’s just 1,000 connects needed. 1,000 connects in a traditional sales organization that’s fully outbound comes from 50,000 dial-outs (2% connect rate).
Converting 50 customers from 1,000 ideal prospects over a year (by reaching out to 50000 contacts) involves perhaps 3 full-time sale reps (while qualifying 200,000 through cold calls takes 12-14 junior reps and a senior rep to take things from there on).
In the traditional approach, all else remaining the same, $1.1M in people costs and $100,000 in data cost would result in $5M revenue.
In the case of predictive, a spend of $545,000 (People cost being $445,000 and Cost of a predictive analytics platform being $75,000) brings you $5M in revenue.
That’s cutting costs by 50%. If your conversion rate is standard, your revenue could be doubled as well!
What we have laid out is a simple but not exceptional case. If you have a paid acquisition budget and an inbound-led marketing engine, the throughput at MQL and SQL stages similarly improve. At PipeCandy, our own targeted paid campaigns to personas identified by our models result in 50% or more CTRs and response rates/MQL rates of over 3% (for our target persona, C-suite & senior executive profiles in marketing at large logistics companies, that is very encouraging). Our CAC to LTV multiple (cost of acquisition of a customer vs. their lifetime value) is 20.
If you are convinced about the power of predictive marketing and yet want to build a solid, objective case for it (or against it), drop us a note here. PipeCandy will help you assess the applicability of predictive marketing at the current stage of your company.