Conversion Rate Optimization
Personalisation and targeting
Adapt the experience where it demonstrably helps, and leave it alone where it does not.
What are you buying when you buy personalisation and targeting?
Adapt the experience where it demonstrably helps, and leave it alone where it does not. In practice that means identifying segments whose needs genuinely differ rather than whose data differs, and then the harder half: targeting rules kept maintainable, which rules out most complex schemes.
Why this work exists
What makes this awkward is that it is obvious in hindsight and invisible in advance.
This is one part of Conversion Rate Optimization, described separately because it is usually the first thing anybody needs.
The nearest piece of work to it is fixing the slowest page on the revenue path first. Where that is already handled, this moves faster than the estimate; where it is not, the two get done together or neither of them holds.
What good looks like
You know it is holding when the numbers stop being argued about and start being used.
The gap between that and where most accounts start is why this is scoped as personalisation and targeting rather than as a line inside something larger. Watching ten session recordings before forming a single opinion is the usual second step once it is in place.
When this is the right work
Start here if you recognise any of these:
- A personalisation tool was bought and is used for one banner
- Personalised experiences were never tested against the control
- Rules have accumulated to the point where nobody knows what a visitor sees
If none of those is true, something else is probably the constraint, and the Conversion Rate Optimization overview maps the alternatives. The usual neighbours are finding the form field that costs more completions than it collects answers and working out what the page has to prove before anyone will act.
What the work covers
The constituent pieces:
- Identifying segments whose needs genuinely differ rather than whose data differs. Worth doing badly rather than not at all, if time is short.
- Targeting rules kept maintainable, which rules out most complex schemes. Skipped more often than any other part of this, and regretted more often too.
- Testing personalised experiences against the default rather than assuming improvement. One pass is rarely enough; the second is much faster.
- Performance and privacy implications assessed before implementation, the step where disagreements surface, which is why it goes early rather than late.
How it runs
Three passes, each ending in something the client can check.
Writing it down for the next person
The reasoning gets recorded alongside the result, so a decision taken in March can still be defended in September by somebody who was not there.
Deciding what not to do
A list of things deliberately left alone, with the reason for each. It saves the same conversation happening four times over the following year.
The second look
A fortnight after the first pass, the same ground gets covered again with fresh eyes. The second pass finds a different class of problem from the first.
What each part runs into
Read against real markets, each part has its own problem:
- Identifying segments whose needs genuinely differ rather than whose data differs: the London version of this has to deal with an enquiry path that runs through a messaging app rather than a form
- Targeting rules kept maintainable, which rules out most complex schemes, where a Los Angeles engagement contends with a catchment smaller than the ambition on the website
- Testing personalised experiences against the default rather than assuming improvement – working in Toronto this means accounting for an incumbent whose position comes from presence rather than effort
- Performance and privacy implications assessed before implementation, and in Jaipur the constraint on it is a buying pattern that resolves faster than the reporting cycle
The mistake most often made here
Personalising for its own sake. Most personalisation does not beat a well-built default, and it adds complexity, latency and privacy exposure.
This is the version most often delivered, which is why the discipline has a worse reputation than it deserves.
How it is measured
Tested uplift from each personalised experience against the default. Reported monthly against a baseline taken before anything changed.
The point of the number is to settle an argument, so it is chosen for being hard to dispute rather than for being flattering.
What you end up with
It ends with a small number of tested personalised experiences for segments that genuinely differ, with maintainable rules.
Short enough to read in a sitting, which is a deliberate constraint rather than an accident.
How it differs by sector
Three cases, each pulling the work a different way:
- Ecommerce and retail, where margin per order decides everything, and the product feed is usually the weakest asset. which moves this work earlier in the plan.
- In Manufacturing and industrial the binding constraint is that low volume, high value, and a specification sheet that decides the deal.
- B2B services: the thing that changes the scope here is that a buying committee, a long cycle, and attribution that has to survive both.
The industry pages carry the rest of that detail. Ecommerce and retail and Manufacturing and industrial are the two where this piece of work most often decides the outcome.
Common questions
Does personalisation improve conversion?
Sometimes, and less often than vendors suggest. It works where segments genuinely need different things, such as a returning customer versus a first-time visitor, or a market with different pricing and delivery. It fails where it rearranges the same content based on data that does not predict need. Both cases require testing against the default to tell them apart.
What are the costs of personalisation?
Latency from the decisioning call, often a flicker of the default experience, maintenance of rules nobody fully understands after a year, privacy exposure from the data collection, and testing complexity because every variant splits the traffic further. These are real and should be weighed against a measured uplift rather than an assumed one. Mention and link reclamation is the counterweight to Sector authority building, and both are worth half an hour.
Is the output a report or a change?
Both, in that order. A small number of tested personalised experiences for segments that genuinely differ, with maintainable rules. The report exists so the change is accountable, not instead of it. Most briefs that mention this also end up touching Los Angeles, and sometimes Manchester.
How do we know this is the right piece of work?
The signals above are the test. If none of them describes your situation, something else is the constraint, and saying so is more useful than selling this. The page that answers the follow-up question is SaaS and technology. The one that answers the objection is Travel and hospitality.
What happens if it does not work?
We report that it did not, with the reason. Tested uplift from each personalised experience against the default agreed before the work starts makes that an honest conversation rather than an argument. What this leaves out is in Analytics and Measurement, and some of it in Answer Engine Optimization.
Ask about personalisation and targeting
Give us the site and one question. If the honest answer is that you do not need this, we will say so.
Have the awkward questions answered first
We will tell you if the answer is that you do not need us yet. That happens often enough to be worth saying out loud.