A Regional Sales Manager of a FMCG company was worried as their competitor was steadily gaining market share across a range of
profitable segments. Despite counter Marketing efforts combined with online
promotion and merchandising range sales kept declining.
When the RSM conveyed to his select few group of senior leaders to find the cause or factors i.e. Marketing Intelligence , dig into competitors practices, they found the challenge ran deeper than they imagined.
Summary as follows:
1.
The competitor had made massive investments
in it’s ability to collect , integrate and analyse data from each store.
2.
These data was used to run myriad real
world experiments.
3.
It had also linked this information to
supplier’s database.
4.
This helped them to adjust prices in real
time to reorder hot selling items automatically.
5.
This analysis also helped to move slow
movers from one store to another .
6.
Continuous testing , bundling, synthesizing
and making information available across the organization was carried out from
store floor to CFO’s office.
7.
This made the competitor’s company far
nimbler and fast decision maker ,
helping it to gain over it’s competitors.
What is Data Mining :
Data Mining refers to the processes and techniques of
extracting data patterns to transform into meaningful structure for taking
rational business decisions.
It is cumulation
of :
- 1. Machine Learning
- 2. Statistics
- 3. Database Systems.
Data Mining Objective : Data mining extracts and forms patterns of
knowledge from large data so as to assist business decision.
Data
Mining consists of :
- 1. Information collection
- 2. Information processing
- 3. Artificial Intelligence
- 4. Machine Learning
- 5. Business Intelligence
Data Mining is the analysis of “Knowledge Discovery in Database” or KDD.
Benefits of Data Mining :
1.
Incremental Revenues :
Analyse the type of products customers
have purchased and capitalise on that insight to personalize expense, increase
customer loyalty and boost customer lifetime value.
2.
Improve Brand value :
Get feedback on successes and failures of marketing efforts through data mining
and take corrective actions.
3.
Increased Reach :
Make the companies outreach the relevant customers with timely and relevant
data mining.
4.
Explore new Markets :
use other database to identify potential customers and conduct relevant out reach.
5.
Trend Analysis :
Compare current data to past information to find trends so as take rational
business decision.
DATA MINING USES IN VARIOUS
INDUSTRIES :
RETAIL:
1.
Retailers use much of data mining information to make decision on Price,
promotions and timings:
2.
After Data mining they also learn what
products sell fast, and might need design / attribute changes.
3.
Mined data on customer behaviour helps
marketing teams appealed promotions to targeted customer groups.
II*
Ecommerce :
Ecommerce :
1.
Ecommerce companies make use of
information gathered from online
purchases and social media habits.
2 .
Companies tap in to data mining to gain deeper insights into their customers.
3.
Using this technology companies like Amazon and eBay recommends products
further and additional sale.
4. Policing and Security Agencies :
1. Police departments these days use
data mining tools to decide where to focus resources.
2. The data patterns surges in specific
crimes in certain areas are analysed and protective measures taken.
3. The data mining tools helps the
policing into strategic and scientifically driven practices.
