This is not a psychic approach with tarot cards or palm readings, this is analyzing buckets of customer buying behaviours and demographics to decide where a customer is in their buying cycle of a product or service.– Computer Dealer News, Jan 2017
Data Mining Analysis Techniques — Challenges
To stay competitive, marketers rely on custom built, specific-purpose, analytics like customer behavior.
Every time you go shopping, you share details about your consumption patterns. And many retailers are studying those details to figure out what you like, what you need, and which coupons are most likely to make you happy. — Forbes, Feb 2012
But in-house reporting tools can’t deliver the deep-dive that marketing analytics require. The roadblock isn’t statistics, but rather, the data preparation effort needed to build actionable analytical data sets — data tables that generate analytics. And this “heavy lifting” effort can include hundreds of millions of data rows.
Far too much handcrafted work, what data scientists call ‘data wrangling’, ‘data munging’ and ‘data janitor work’, is required. Data scientists spend from 50% to 80% of their time mired in this mundane labor of collecting and preparing unruly data. New York Times, Aug 2014
But, these processes are critical to discovering new data relationships, creating new transformations, and developing marketable analytics. Many business analysts, although Excel wizards, lack the data management skills. And without these skills, they never discover the right data interactions among customer, brand and competitor.
It’s not whether companies have enough data about their customers; what matters is how they use the data. Data has become a “competitive weapon” that give businesses an advantage over competitors. It’s about delivering a continuity of experience and giving customers what they want… better and faster than competitors. — 1to1 Media, Oct 2015
Marketers are also swamped by increasing data volumes and by complex data structures. It’s also true that many marketing executives aren’t sure of the data they have. They don’t believe they have the data they need. And what they do have, they question the quality. Or worse, can’t access. And there are tons of other roadblocks. Data is fragmented. There’s no linking data across channels. A lack of internal skills. And, of course, data quality is poor.
Data Mining Analysis Techniques — IDP
Effective use of customer data is a strategic, competitive weapon. But, your data is challenging and you have limited resources. Well you can’t send an algorithm over raw data and have insights pop up.
Data mining and knowing what the customer wants before they do is a business necessity. — Computer Dealer News, Jan 2017
At IDP, we remove the requirement that business analysts manage large data volumes or complex data structures. Bridging the gap between data management and analytics. So analysts focus, not on data preparation, but on making business recommendations from the analytics.
We integrate disparate data sources and migrate large data volumes with complex structures to build rock-solid analytical data sets. And of course, we always collaborate with marketing to produce consistent, marketable results.
IDP empowers marketing executives to take control of their marketing data, their analytics and their marketing technology.