10 Hosted CRM Features That Will Help You Build Customer Loyalty

How to improve close rates and average deal size with the right CRM solution and features.

Hosted CRM solutions are not all created equal. Some cater to a company’s sales force automation needs, while others focus on marketing campaigns and forecasting. There are vendors that offer both hosted and on-premise CRM tools, and those whose bread and butter is strictly delivering on-demand offerings. But regardless of a solution provider’s area of expertise, all of today’s CRM solutions should include the following features:

  1. Lead Management: Lost revenue is often a result of allowing prospects to fall through the cracks. With lead management, however, every lead is promptly routed to the right salesperson. What’s more, leads can be tracked and managed through the entire sales cycle, from initial identification to final sale.
  2. Feedback Management: By capturing customer feedback across all channels of communication, salespeople can capture a better understanding of the needs, wants and buying patterns of their customers. Furthermore, armed with feedback from countless touch points, a company can establish the processes required to deliver an optimum customer experience.
  3. Order Management: When more than one department plays a part in processing an order, the margin for human error grows, as does the mound of paperwork. With order management, however, quotes are easily converted to orders, modified and saved in a single system.
  4. Territory Management: Keep your sales reps from stepping on each others’ toes with territory management, which easily creates sales territories and manages territory-based processes with workflow rules and reports.
  5. Email Management: One surefire way to anger your customers is by failing to respond to their emails. These days, email correspondence is used to log complaints, issue requests and offer feedback. Fortunately, email management can chronicle customer-related communications with automated tracking of customer emails. As a result, emails can be responded to in a timely fashion, and end users can establish alerts if a message has not been handled within a predetermined time frame
  6. Contact Management: When it comes to staying on top of your customers, Microsoft Outlook is simply not enough. That’s why most CRM solutions include a contact-management component to provide employees with a complete, 360-degree view of their customers. Sales reps can view all contact and account information, as well as a customer’s purchasing history, from a central location. And reps can better manage their to-do lists by establishing alerts notifying them of upcoming tasks and events so that each customer is treated in an individual manner.
  7. Reporting: From standard templates to heavily customized documents, CRM tools can generate detailed reports featuring contact information, opportunity pipeline information, lead-status analyses and specific customer case studies. In the end, these reports are an ideal way to organize a company’s collection of customer information and insights.
  8. Opportunity Management and Forecasting: Don’t promise what you can’t deliver. Opportunity management and forecasting provides a complete view of the sales and production pipeline so that businesses can accurately and quickly handle the orders they’re generating.
  9. Marketing Campaign Analysis: How do you know if you’re getting the bang for the buck you’ve invested in a marketing campaign? CRM solutions can help monitor and analyze your advertising efforts, from trade shows to direct mail, so that every marketing dollar spent is spent wisely.
  10. Marketing Revenue Tracking: So positive customer feedback isn’t enough to convince upper-management that a costly marketing campaign produced results? With marketing revenue tracking, a company can identify the marketing activities that generate the most sales revenue by directly linking every sales dollar back to its related campaign.

Reference: http://www.insidecrm.com
photo credit: aolin via photopin cc



Using CRM Software to Increase Sales


How CRM can help you plan, achieve and manage your sales better

No sales, no business. It’s as simple as that. Finding new leads, negotiation, customer acquisition, post-sales support and sales planning are all too important to leave to chance. Using the appropriate customer relationship management (CRM) software can help you make the most of your customer data and help your salespeople do better. Even in very small companies, using CRM software to coordinate sales can bring big dividends.

How can CRM help the sales function?

  • Customer Data in one place. Sales teams spend too much time putting together customer data stored in different locations. CRM software lets people access data on customers’ past purchases, behaviour, preferences, usage as well as demographic and contact information quickly. Regularly updating this data ensures that sales teams do not have to scramble for information at the last-minute before a call or a meeting.
  • Qualifying Leads. Not every lead converts into a sale. So the question is: how do you improve the ratio? CRM software can track past performance and identify metrics, for example, past purchase value or demographic indicators such as income or age, that indicate which leads are ‘hot’ and which are not. This allows you to devote more attention to the best opportunities.
  • Cross-Selling. With better and more updated knowledge of customer behavior and preferences, salespeople have a higher chance of re-selling or up-selling to existing customers.
  • Manage Cash Flow. All businesses and especially small- to mid-sized ones find predicting and managing cash flow one of their biggest challenges. Using CRM software gives businesses a clearer picture of the sales pipeline. How many leads exist? Which are likely to convert to a sale? CRM helps you answer these questions.
  • Team Management. You can more easily track your team’s activities. CRM lets you see who is performing well and who needs help. It can also simplify bonus calculations by giving detailed reports on sales. More importantly, because everyone has access to the same data, teams can avoid mistakes, oversights and delays.
  • Future Planning. Modern CRM systems provide for detailed reporting, including the ability to link sales results with different inputs such as campaign spends, customer research scores or sales staff employed. This can help businesses analyse the cause of both success and failure, and plan better for future rounds of sales activity.

Ref: http://h41112.www4.hp.com/promo/obc/uk/en/business-it-advice/increase-your-sales/using-crm-software-to-increase-sales.html


Why Data mining in CRM?

“CRM is about acquiring and retaining customers, improving customer loyalty, gaining customer insight, and implementing customer-focused strategies. A true customer-centric enterprise helps your company drive new growth, maintain competitive agility, and attain operational excellence.” SAP

Customer Relationship Management (CRM) is a business philosophy involving identifying, understanding and better providing for your customers while building a relationship with each customer to improve customer satisfaction and maximise profits. It’s about understanding, anticipating and responding to customers’ needs.

To manage the relationship with the customer a business needs to collect the right information about its customers and organise that information for proper analysis and action. It needs to keep that information up-to-date, make it accessible to employees, and provide the know how for employees to convert that data into products better matched to customers’ needs.

The secret to an effective CRM package is not just in what data is collected but in the organising and interpretation of that data. Computers can’t, of course, transform the relationship you have with your customer. That does take a cross-department, top to bottom, corporate desire to build better relationships. But computers and a good computer based CRM solution, can increase sales by as much as 40-50% – as some studies have shown.

This is where Data Mining, Artificial Intelligence, and intelligent search applications come in. Wait, back up a minute, what are all these terms, you ask…

A good CRM application will provide the facility for the business to store and manage data they collect on their customers, and products. A better CRM will have the ability to group the data, convert them to information and display them in its search results whenever a user types in a word that may match the group of keywords associated to the question.

Okay, before we proceed, let’s get an understanding of what data mining is about…

Data mining, a branch of computer science[1] is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.

The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques to sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations.

Basically data can be collected, they would just be random numbers and words. Arranging this data into meaningful information was a tedious and arduous task for people compiling the data. The theoretical knowledge from the statisticians were converted into programming languages and data mining applications were developed in due course.

The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection, storage and manipulations. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns.[2] It has been used for many years by businesses, scientists and governments to sift through volumes of data such as airline passenger trip records, census data and supermarket scanner data to produce market research reports. (Note, however, that reporting is not always considered to be data mining.)

A primary reason for using data mining is to assist in the analysis of collections of observations of behaviour. Such data are vulnerable to co linearity because of unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole domain, and therefore may not contain examples of certain critical relationships and behaviours that exist across other parts of the domain. To address this sort of issue, the analysis may be augmented using experiment-based and other approaches, such as Choice Modelling for human-generated data. In these situations, inherent correlations can be either controlled for, or removed altogether, during the construction of the experimental design.

Data mining commonly involves four classes of tasks:[12]

  • Clustering – is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
  • Regression – Attempts to find a function which models the data with the least error.
  • Association rule learning – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

With technology growing in leaps and bounds, Data mining has been considered to be added into customer relationship management applications. Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimise resources across campaigns so that one may predict which channel and which offer an individual is most likely to respond to — across all potential offers. Additionally, applications could be used to automate the mailing. Once the results from data mining (potential prospect/customer and channel/offer) are determined, this applications can be programmed either automatically to send an e-mail or regular mail or with the few steps a user has to click a button and mails to customers can be sent in bulk. Of course, the issues of bulk mail and spamming should be given due consideration here, it would be at the onus of the business to ensure their mass mailing is not construed as spam.

Finally, in cases where many people will take an action without an offer, uplift modeling can be used to determine which people will have the greatest increase in responding if given an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set.

Businesses employing data mining may see a return on investment, but also they recognise that the number of predictive models can quickly become very large. Rather than one model to predict how many customers will churn, a business could build a separate model for each region and customer type. Then instead of sending an offer to all people, who are likely to appear on the search, it may only want to send offers to customers. And finally, it may also want to determine which customers are going to be profitable over a window of time and only send the offers to those that are likely to be profitable. In order to maintain this quantity of models, they need to manage model versions and move to automated data mining.

An example of a CRM application would be in a car manufacturing business (assuming they sell directly to end users). If they maintained a database of which customers buy what type of product, and when, how often they make that purchase, what type of options they choose with their typical purchase, their colour preferences, whether the purchase needed financing etc., the manufacturer knows what marketing material to send out, what new products to promote to each customer, what preferences/options may swing the sale, whether a finance package should be included in the marketing material and when would be a good time to target each customer. They could use the information to build a relationship with the customer by reminding customers of service dates, product recalls, and maybe even to send the customer a birthday card.

A good place to start would be to make a list of your objectives and the benefits your organisation hopes to achieve. When looking at CRM solutions you want to check the features and functionality “out of the box”

– customisation is all very nice but it takes time and may not be as easy as you think

– supported platforms in terms of hardware, operating systems, databases, online activities and online ordering systems etc., (not just your back office systems but third-party software you use too)

– integration with those systems

– global perspective

– price – preferably a one-off purchase price with no annual licence fee.

Therefore, if you are looking to grow your business in leaps and bounds, and you know the way to it is to grow your customer base, to improve your relationship with your customer, to actually be able to get insights on your customer buying behavior and pattern, then you need a CRM application.

Not just any CRM application. A CRM application that can collect the right information about the customers and organise that information for proper analysis and action. An application that is able to keep information up-to-date, is accessible to employees, and the employees have the know how for  to convert that data into products to better matched the customers’ needs.

The secret to an effective CRM package is not just in what data is collected but in the organising and interpretation of that data. Computers can’t, of course, transform the relationship you have with your customer. That does take a cross-department, top to bottom, corporate desire to build better relationships. But computers and a good computer based CRM solution, can increase sales by as much as 40-50% – as some studies have shown.


^ Clifton, Christopher (2010). “Encyclopedia Britannica: Definition of Data Mining”. http://www.britannica.com/EBchecked/topic/1056150/data-mining. Retrieved 2010-12-9.

^ Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN 0471228524. OCLC 50055336.

^ Alex Guazzelli, Wen-Ching Lin, Tridivesh Jena. PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics. CreateSpace, 2010

^ a b The Data Mining Group (DMG). The DMG is an independent, vendor led group which develops data mining standards, such as the Predictive Model Markup Language (PMML).

^ PMML Project Page

^ Alex Guazzelli, Michael Zeller, Wen-Ching Lin, Graham Williams. PMML: An Open Standard for Sharing Models. The R Journal, vol 1/1, May 2009.

^ Y. Peng, G. Kou, Y. Shi, Z. Chen (2008). “A Descriptive Framework for the Field of Data Mining and Knowledge Discovery”. International Journal of Information Technology and Decision Making, Volume 7, Issue 4 7: 639 – 682. doi:10.1142/S0219622008003204.

^ Proceedings, International Conferences on Knowledge Discovery and Data Mining, ACM, New York.

^ SIGKDD Explorations, ACM, New York.

^ International Conference on Data Mining: 5th (2009); 4th (2008); 3rd (2007); 2nd (2006); 1st (2005)

^ IEEE International Conference on Data Mining: ICDM09, Miami, FL; ICDM08, Pisa (Italy); ICDM07, Omaha, NE; ICDM06, Hong Kong; ICDM05, Houston, TX; ICDM04, Brighton (UK); ICDM03, Melbourne, FL; ICDM02, Maebashi City (Japan); ICDM01, San Jose, CA.

^ Fayyad, Usama; Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996). “From Data Mining to Knowledge Discovery in Databases”. http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf. Retrieved 2008-12-17.