1. Observe patterns, don't just collect data

Everyone has their comfort actor and RDJ is mine (I’m a big Iron Man guy) so when I couldn’t sleep this weekend, I decided to rewatch Sherlock Holmes. This time I started to clock some of the nuances that I hadn’t the first time I watched — His whole thing is turning tiny clues into major revelations. I was watching this guy and thinking, “I would have hired him as a sales rep.”

Holmes can look at a person's watch and tell you their entire life story, not just “he was wearing a watch” or even “he was wearing a very expensive watch.” 

Valley can look at someone's website behavior and predict their whole buying journey. Different century, same skills. Both are done by, not just seeing, but really observing your target. 

Sherlock always tells Watson, "You see, but you do not observe." The same goes for website data. The key is taking the information that you’re given and analyzing it to best predict your prospects' pain points, intentions, questions, hang-ups, and ultimately their next move. 

2. Connect seemingly unrelated signals

Great, you’ve clocked the pattern, and patterns repeat themselves so you’re all good to go, right? Wrong. 

Sure, finding the patterns can be the widest net you cast, but in a world of “never let them know your next move,” you have to be able to dig a level deeper and catch variations from the classic “I have a problem, I’m interested in the way you solve it, I want your help in solving my problem” mold. 

Here are examples of signals that would be missed by routine pattern detection because they seem too disparate, and what they actually mean:

Signal 1: Prospect views your enterprise pricing page

Signal 2: Their LinkedIn shows recent promotion to VP

Signal 3: Company just raised Series B funding

Deduction: They're looking to make a big move with their new budget authority

OR

Signal 1: Multiple visits to your API documentation

Signal 2: Engaging with "migration guide" content

Signal 3: Their company's job board shows developer openings

Signal 4: Their competitor just announced a price increase

Deduction: They're evaluating switching costs from a competitor

3. Let AI be your magnifying glass

You’ve filtered for patterns and picked up on some more niche buyer signals, this is where the charm comes in. Why being able to read people and intuit their needs and next moves is important. Your job is translating those social skills and that deductive prowess into the digital world. Holmes famously said, "From a drop of water, a logician could infer the possibility of an Atlantic or a Niagara."

For example: 

If—Someone visits your pricing page, then immediately checks out your case studies.

Then—They're not just "browsing." They're “following the trail” and building a business case.

If—Someone spending 15 minutes on your technical documentation.

Then—They're doing due diligence. They’re interested and are taking the next step. 

If—Someone keeps returning to your site and opening specific pages in new tabs

Then—You’ve got multiple footprints at the scene. They’re literally telling you what they are interested in or having trouble understanding. 

They are handing you the evidence on a silver platter and saying “Here. This is the best way to read my mind and sell me on your product.” 

A look under the hood:

With Valley’s V3, we’re trying to mimic this “charming detective” persona to really target the prospects that are most likely to convert and then get to know them well enough to know exactly what to say to make sure they do. 

We Gather the EvidenceTrack every page visitMonitor time spent on each sectionRecord download patternsNote referral sourcesConnect the DotsSocial media engagementEmail interactionsWebsite behaviorContent consumptionMake DeductionsInfer buying stageUnderstand pain pointsPredict decision timelineBudget RangeAct on Intent SignalsCreate a profileTrigger a timely, personalized outreach messageSuggest the next best actions

The action is the final step—

4. Time your intervention perfectly

You now know your prospect’s habits, company size, industry, recent funding, tech stack, pain points, interests, and more. The last step is letting your model connect these dots automatically, understand the story that they tell, and then trigger the right action at the right time.