How Data Mining Can Help Salesforce?

How Data Mining Can Help Salesforce

A World Of Data

There are three basic ways of selling a commodity. You can do it door to door, you can do it through cold calling, or you can sell it in a market. Chances are every business utilises all of three of them in some form or the other. Yet, is it the complete solution? Which residences would you choose to move door to door? Which type of people will you call to sell it and which market would be the best suited for the commodity?

These are pivotal questions that define how many sales are made. If you choose the wrong place or person, the outcome is zero sales. This is where data steps in saves the day. With the right, processed data, one knows precisely which audience to target and how to reach them. Moreover, you get the answers to :

  • what the product is?
  • who is interested in buying it?

The result is excellent sales because data offers insight into a lot of things like gender, age, and geographical location. Currently, almost every business runs on data. The only condition is utilising it accurately. Check Out – more insights about the role of data mining in CRM.

The Pull Of Data Mining & CRM

Yes, data is vital. Yes, there is a lot of it available, but it isn’t enough. One needs to convert the bunch of numbers into useful information that can be used for appropriate channels. Data mining is the process that transforms raw data into information. It has to be repeated at set intervals because the data can change from time to time, resulting in different information. The information only leads to better sales if the data input is accurate and reliable.

Graphical Illustration of Data Mining & CRM Concept

To explain in easier terms, data mining gives businesses a plan that helps define the sales process. The blueprint sometimes also assists in administration and policy-making of the company. Currently, there are two kinds of data mining processes. The first is automated, and the second is manual. The latter is considered to be more accurate because it uses three approaches to identify patterns in data:


  • critical thinking
  • logical approach
  • human intervention

These are not included in the automated data mining procedure which operated on a determined algorithm to find repeated behaviours.

Now the importance of data and data mining is evident; we come to CRM. Customer relationship management is a tool businesses use to maintain a long relationship with their patrons by documenting sales. CRM contains a wealth of data which mined accurately can give companies deep insight to expand the business. Without mining, the complete value of the data is left untouched.

When CRM tools are clubbed with data mining, hidden trends, and features come forth. These trends help in:

  • pinpoint problems with customer relationships
  • predict future sales and trends
  • give an overview of the sales
  • add value to future transactions by crossing-selling products

Graphical Illustration of Data Mining Add on

Salesforce CRM And Its Data Mining Add On

Currently, there are several customer relationship management software present in the market, including Salesforce CRM. While the tool along gives incredible value to a business, they are still under-utilised because they rarely have data-mining feature. As a result, data is collected but not completely understood. This where adds-on aid. By installing a data mining add-on to an already present Salesforce CRM, functionality is increased by a manifold.

Yes, the salesforce CRM has analytical functionality, but its reporting is limited. By adding data mining to it, you can pull the entire data to create tailored reports such as segmentation and forecasting. The reports with mined data can be employed by workers to select trends and capitalise on them.

In the present age of technology, data is a valuable currency. A business who mines it will expand to the zenith. It is high time you combine your Salesforce CRM with a data mining tool to leverage the information just beyond your fingertips.

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Significance of Data Mining in the Retail Industry

Illustrated Image of Indian Retail Industry

Data Mining In Retail Industries: The Future

There is no doubt that data has been a game-changer for retailers for years. To understand the retail lifecycle, the industry experts and shop owners have long collected data from all points of contact. Earlier, the information was gathered from order books and bills, then came invoices, sales points, and purchase areas. Finally, it shifted to Retail POS Software Solution and inventory management systems. The data collected was put to one use: to predict what the customers will buy next.

These methods of data gathering don’t work now. The retail industry is inundated with information. The data created from average sales and online shopping history is expanding at a furious pace. The ease of availability of products through e-commerce sites has exploded information. It signifies that a new method for data collection and interpretation was required.

What Is Data Mining In Retail Sector?

As technology boomed, retailers were able to move from guesswork to a more scientific approach to data interpretation and collection — door to door surveys transformed into online and personalised ones which morphed into cookies and data crawlers. The latter is automated technology that tracks the moves of online shoppers and amass data. These technologies accumulated mountains of data which needed proper interpretation. It is here that data mining steps in the field of retail. Check Out the role of data mining in retail sector.


Significance of Data Mining in Retail Industry

Called data discovery or knowledge discovery, data mining is the procedure of turning raw data into useful information. The process necessitates:

  • Analysing the raw data from many perspectives and angles
  • Categorise the data and summarising it into usable info that is then utilised to:
      • Cut cost of operations
      • Increase revenue
      • Do both

Many analytical tools can be employed to analyse data, out of which a data mining software is one. In simple terms, data mining pinpoints patterns between the hundreds of fields included in the retail sector. The software crawls through dozens of databases and finds correlations such as the beer-diaper connection which showed that men who buy diapers tend to purchase beer with it.

  • The significance of data mining in retail comes from the benefits it offers, which are:
  • Decrease the cost is borne by a business
  • Identifying the behaviour of the consumer
  • Exploring the trends in retail and pattern of shopping by customer
  • Bettering the quality of service to patrons
  • Improve customer satisfaction
  • Enhance customer retention
  • Augment the return on investment to business

An example that helps explain the value data mining brings to the retail sector is new customer acquisition and old customer retention. We all know that the retailers are plagued with the competition. Losing customers is common. The one way to retain them is to understand what they wish to buy and give them the incentive to purchase the same. Through data mining, one can use detailed sales history to pinpoint where to target the customer and hence retain the patron.

Embracing Data Mining in Retail has significant potential to drive business

Data Mining: Not A New Technique In Retail

Retailers and shop owners have been mining data for years to improve business. The term is relatively new, but the technology is old. The difference is that the tools used to extract or discover knowledge have changed. First powerful computers were employed to sift through large volumes of reports to find trends and patterns; now, software are utilised to do the same.

The improvement in technology, such as immense storage space, better statistical software, and high processing power, has made data mining considerably accurate. The precision has brought down the cost borne by retailers, too.

Any retailer who wishes to gain an edge on their competitors must appropriate data mining. Software that helps in data mining give your precise information on every segment of the industry. From how to maintain customer relationship to how to improve the performance, from sales trends to buying habits of all patrons, there is no inch data mining leaves unseen. Preferences, peak traffic hours, seasonal changes, delivery times, and lead period are just the tip of the iceberg data mining can uncover.

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Is Mining Search Keywords Some Kind of Data Mining Task?

Recently I noticed there are many keyword research tools are available in digital marketplace which help to identify niche keywords for promoting or advertising of specific product. Some businesses also lately eager to find search keywords which are relevant to their product or service promotion. Among other reason is because optimizing search keywords is critical to get a higher ranking in the current search engines. In term of data mining task, the data source here are the biggest on-line database (WWW), specifically search keywords of the available search engines.

Before we start, let me explain why search keywords are important to many search engines. For example, Google’s seach engine does the work for you by searching out Web pages that contain the keywords you used to search, then assigning a rank to each page based on several factors, including how many times the keywords appear on the page. Higher ranked pages appear further up in Google’s search engine results page (SERP), meaning that the best links relating to your search query are theoretically the first ones Google lists.

According to the Microsoft adCenter Labs, keyword research technology goes beyond traditional data mining because of the scale and scarcity of the data. For example, in the study of keyword semantics alone, there are many potential relationships such as synonymy, antonymy, similarity, membership, attribute, causation, hierarchy, and substance. Tools in the Keyword Research category exploit such relationships between the keywords.

We know that data mining tasks include classification, association rules, dependence modeling etc. Well, I can say that at least research keyword tools are belong to association rules task because this data mining tool provides a graphic view of the associations between entities by mining the co-occurrences of entities in search queries or search sessions. Such associations will be useful when creating query suggestions or analyzing user search patterns.

I have managed to try one keyword research tool available in the market called Keyword Research Pro. Keyword Research Pro pulls keywords from the biggest databases online, adding up to several million keywords. No other keyword tool is able to pull keywords from so many different keyword sources. In addition, it has a built-in ‘auto-digging’ feature that will dig down multiple layers of keywords to return to you the desired number of keywords you require. So for example, if you require 1,000 keywords for ‘weight loss’, Keyword Research Pro will fetch keywords and ‘auto dig’ until it returns you 1,000 ‘weight loss’ related keywords. Keyword data sources include:

– Google AdWords Keyword Tool
– Keyword Discovery
– Wordtracker
– 7Search
– Miva
– YouTube
Keyword Research Pro will not only tell you how popular any keyword is, it will also tell you how much competition there is, how many people are advertising on those keywords, how much they are paying, their actual ad copy, you can even view your competitors actual landing pages!

Keyword Research Pro Analysis Options:
– Competition: Google, Yahoo & Bing (Phrase or Broad Match, AllInTitle (default) & AllInAnchor)
– Search Volume: Google (default), Wordtracker, 7 Search, Keyword Discovery
– Keyword Effectiveness: Keyword Effectiveness Index (default) & R/S Ratio
– Pay-Per-Click Info: Average Cost-Per-Click (Broad, Phrase, Exact) and Competing Ads (Analyze competitors’ PPC ad copy and actual landing pages with ease!)

For More Information about Data Minining click here

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