Our Process

Our Process

Audit

Imagine for a moment that your organization's data is a hoard of mismatched socks stuffed under a bed. It's been piling up for quite some time, occasionally developing sentience and forming questionable alliances with half-eaten sandwiches.

The purpose of this phase in the process:

• Root through every single (and singularly suspicious) data source - CRMs, ERPs, websites, social media streams and that ancient spreadsheet that no one's dared open since 1992.
• Check if they're playing nicely together or staging sock rebellions.
• Note any compliance issues (if any sock is actively rebelling, we'll want to label it clearly so your data compliance staff don't get attacked by ankle-high terrors).

At the same time, we'll want to peek at the business's public face. Does the website promise cutting-edge innovation while the marketing emails look like they were lovingly crafted in 1995 using Comic Sans and clip art of dancing hamsters? Such revelations are crucial. Like Dave from accounting who claims they've hit an eagle at Fancourt, but we've seen them at the driving range - we know they're game ain't all that - and we question if they've even played at Fancourt. C'mon Dave, we know what you're about. Equally, brand mismatch issues must be handled - preferably before you hit the golf course (or before the customers notice).

Build

Now that we've rummaged through the sock drawer, it's time to weave something wearable from the chaos. We call this stage "BUILD", but one can think of it as forging the One Dataset to rule them all (only with fewer volcanoes and slightly less malicious intent).

Here's how we tackle this phase:

• Create a Unified Repository: Whether we call it a data lake, data warehouse or data jacuzzi, the point is the same - get all that data swimming happily in one place.
• Automate and Normalize: Because manually copying and pasting from 47 spreadsheets at 2 a.m. is best left to Excel addicts who love spreadsheets just a little too much - and we prefer our data modelled, not modded.
• Data Governance: A fancy term meaning "Don't let the sock rebellion happen again #NeverForget". Also ensures we're obeying privacy laws, not spamming unsuspecting parents of teenagers with baby clothing catalogues and tracking data lineage so we know where what comes from.

When this is done, you end up with sparklingly accurate customer profiles. It's like each pair of mismatched socks is now happily matched (and politely labeled "Left" and "Right", so they don't argue).

Predict

We arrive at the stage where you attempt to foresee the future without having to resort to witchcraft and living sacrifices (or at least that's what our data scientists promise us). Data analytics is the modern-day crystal ball, except it uses strange symbols from ancient languages in even stranger formulas and algorithms that requires data contracts between obscure sources.

In this mystical domain, you'll find:

• Propensity Models: "How likely is Customer X to purchase that inflatable dinosaur costume next month?"
• Forecasting Models: "Will we need more inflatable dinosaur costumes in the next holiday season?"
• Recommender Systems: "If you like inflatable dinosaur costumes, you may also enjoy pterodactyl slippers."

These cunning contraptions allow you to anticipate customer desires and intervene with just the right offer. Instead of waiting for them to wander off in search of a better-fitting dinosaur outfit, you nudge them gently: “Hey, we've noticed you might be in the market for prehistoric apparel - here's a discount code for 10% off your first roar.”

Scale

At this point, you've become so adept at weaving data magic that you're sending out personal messages en masse: "Dear Martha, we know you love whiskey and enjoy reading late-night dramas on your kindle. May we recommend some dark chocolate to go with your guilty pleasure?" But eventually, you need infrastructure - like the marketing equivalent of an ever-expanding TARDIS (bigger on the inside, also in questionable compliance with building codes).

Scaling Tactics to Consider:

• Marketing Automation: So your staff don't collapse from exhaustion writing billions of uniquely personalized emails for every eventuality.
• AI-Driven Engines: Real-time data processing that can decide if a customer is more in the mood for wine and cheese or whiskey and chocolate.
• A/B or Multivariate Testing: Because sometimes you're not sure if chocolate paired with romcoms out-sell a pairing with dark romance novels, and you want to test both at scale.

And don't forget the people! Train them, guide them, feed them (occasionally with actual food). Ensure they know who's responsible for which tasks, so you don't end up with a departmental meltdown worthy of a comedic tragedy.

Improve

Here's where you institutionalize your newly formed habit of never, ever, EVER being satisfied. You set up feedback loops faster than Max Verstappen at Monza. You build real-time dashboards that reveal in neon-bright clarity when something's amiss - like if your new troll-proof hat campaign is unexpectedly popular with wizards instead.

Why you should love this phase:

• Instant Visibility: Spot trends, anomalies, and suspicious lumps in your data the moment they appear.
• Model Maintenance: Predictive models are only as good as the fresh data they're fed. Keep them well-supplied or risk the dreaded “stale prophecy” effect.
• Team Growth: Continuous learning. By the time your staff are done with all these workshops and courses, they'll be able to conjure marketing strategies from thin air like a wizard summoning a midnight snack.

Eventually, your marketing team attains a form of enlightenment where improvement is no longer an event but a constant state of being - like breathing, blinking, or quietly rummaging for more data to feed the insatiable analytics beast.

Finally

And there you have it, dear traveler: the DVC Process, a cyclical marvel that starts with a rummage (AUDIT), proceeds to data alchemy (BUILD), then peers into the future (PREDICT), expands into multiple dimensions at once (SCALE), and concludes in perpetual motion (IMPROVE).

Remember:

• If you try walking a mile in someone else's shoes, you'll likely be arrested for shoe theft.
• If you try building data-driven customer profiles, you'll likely be rewarded with better marketing strategies and the gratitude of your now properly shod customers.

Follow the cycle, iterate like a caffeinated hamster on a wheel, and watch as your marketing department transforms from a cost pit into a profit-powered wonderland (complete with the occasional dinosaur costume cameo). In the end, you'll find that the journey is filled with enough humor and possibly misplaced footwear to keep even the most jaded individuals thoroughly entertained.

Unquestionably useful principles:

1. Data Rules All: If the data is wrong, the strategy will be too.
2. Customers First (Always): Happy customers, happy life.
3. Brand Togetherness: No one likes a patchy identity.
4. Profit is Lovely: Marketing is an investment if your brand is seen/used as an asset, not a black hole.
5. Tinker Forever: Improvement is a journey, not a destination.