Most of us are aware Master Data Management is not just managing data, but it’s beyond just MDM, it actually requires use of technology, data integration tools & processes to create and maintain consistent and accurate list of master data in organizations. Master Data Management or MDM typically called as might become a nightmare for many organizations either large or small if it’s not planned and implemented at the right time. So how organizations need to plan for it?

3 Steps to Plan for Master Data Management

1. Identify Master Data Requirements – First and foremost thing when you have decided to go for master data management is the need to evaluate your existing systems, processes, infrastructure, organization structure and more, then try to find out if your organization actually requires an MDM and start identifying the requirements by asking questions as what’s are the business values to be achieved with this information from MDM & more.

2. Business Process Requirements – Once you have identified your MDM requirement your next immediate things to do is to start asking questions related to business process like what needs to be managed, who would be managing the MDM, what are the processes to be considered, what are the integration tools required, who can access the information, what’s the format of display, who can alter or edit the information, how to maintain the master list should it be in single format or multiple formats/copies, what locations to be maintained and more.

3. Type of Analytical requirements – Next would be to find out what types of analytical requirements for your organizational MDM plan like for areas as business intelligence & management reporting for accurate information for various roles and responsibilities.

From these steps and information start building the MDM project plan & look out for vendors like IBM, Informatica, Oracle, SAP & others who can implement MDM for your organization. During the next few years Master data management might become mandatory due to increase in mergers & acquisitions worldwide. Developing a strong MDM for large or small business helps in organizations success.

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Most of the large & medium size manufacturing companies & retail chain stores have inventory forecasting software’s for forecasting inventory & future sales number based on past data, industry trends & economic conditions All statistical based inventory and sales forecasting models or software’s aren’t 100% accurate but have you ever thought this inventory forecasting software’s fail completely.

 3 reasons for failure:

• Negative sales – In many products based organizations sales happens during last few days of the month or year for achieving numbers. Few of these sales are called negative sales as they would be reversed back at a later date. Does your sales and inventory forecasting software accept stock reversing if yes then it failed to forecast sales accurately? So the sale happened isn’t actual sale and all forecast done for future dates in invalid due to negative sales. Do you still rely on the data?

• Promotional offers – Does your sales and inventory forecasting software consider your organizations promotional offers while forecasting sales and inventory? If yes, what’s the base for such forecasting as each promotional offer is unique to attract more and more customers and varies depending on the sales trend, market scenario and geography?

• Newly Launched products. All products have specific product life cycle from launching stage, growth stage, maturity stage and finally phase out stage. But few products are launched which wouldn’t fall into any existing product category as they have been launched to create a new product category itself like world cheapest car. In such new launches does your forecasting software forecast sales & inventory. If yes what’s the basis for such forecasting or does it considers competitors most similar products. If yes it’s not forecasting for your product but for your competitor products. Do you really need such forecasting data?

Considering these facts under which sales and inventory forecasting software’s fail, let’s assume we would soon have sales and inventory forecasting software which forecasts up-to 99.99% accurately.  Till then we need to rely on our old forecasting software’s only.

For detailed sales &inventory forecasting software visit www.mydbsync.com 

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Quality of data plays a major critical role in industries like retail, telecommunications & financial services with large customer base.  Most organizations falling under these industries in a rush to capture market share and to add new customers give secondary thought to quality of data captured, leading to substandard customer data with inaccurate, duplicate & outdated customer details.

Inaccurate or poor customer data is due to non standardization of data capturing process or format, multiple data capturing points across the organization & data storing in incompatible systems and formats.

Organizational impacts due to poor data quality

• Business performance – Due to duplication of data it’s difficult for marketing to cross sell other products and services, profiling and segmentation of customers leading to decrease in business performance
• Customer retention – Due to poor data quality cost of customer retention is high as it requires multiple interactions with customers to understand their actual requirements which would have been solved with  accurate data and past transaction details
• Customer Mapping – Due to multiple entries of same customer with contradicting details it would lead to wrong mapping of customer s with wrong product or service leading to valuable customer loss and revenue to organization

So organizations with large customer data should ensure their data capturing methods are standardized across all data capturing points with latest data integration software for a satisfied customer and profitable organization.

Learn more about data integration by visiting www.mydbsync.com

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Compared to few years back we are now in an ever changing business world on real time basis and to this fast changing dynamic world even retail chain stores from hyper markets to smaller retail stores & manufacturing companies have implemented sales & inventory forecasting software for quick and easy forecast either web or desktop based. Most of these forecasting software’s are developed with all the modern statistical forecasting techniques with features as inventory in hand, forecasting inventory required, scalability, stock level alerts & minimum customization.

But what’s the next level of forecasting for more accurate inventory levels?

• Anticipating the unexpected – Few of the larger retail chains around the world have customized their inventory forecasting models with anticipating unexpected and alternatively  have incorporated solutions in forecasting software. But what about the same features availability for small to medium size retail stores. If yes at what cost?

• Collaborate with suppliers in sales, planning and inventory forecast – In today’s scenario it’s impossible for any manufacturer to manufacture all the items they require and the same has been outsourced to suppliers. Does your inventory planning software consider suppliers inventory too for your organizations inventory forecast? If not include them

• Include your customers in planning your sales & forecasting inventory – All types of retail stores and manufacturing organization have loyal customers with regular purchase patterns.  Can your inventory forecasting software include your customer’s future requirements by understanding their purchase patterns? If yes then your forecasting software is ahead of it’s time

All the sales & inventory forecasting software’s currently available in the market do have forecasting sales & inventory capability confined to internal organizations inputs* only. If these software’s are included with all the next level forecasting inputs at affordable prices then it would change the way forecasting in done by collaborating with partners, suppliers & customer’s on real time basis.

For more detailed sales & inventory forecasting software visit www.mydbsync.com 

Learn more about DBSync & Lokad

Data, as much as it is a lifeblood of our business, can also be Achilles Heel for various reasons, sometimes reasons best known to none! If you are in the Business Data Integration stream, the complexity only multiplies exponentially. When you are selling an integration product, you are by default understood as flawless integrator of data regardless of any factors. But we are not living in an ideal world are we?

More often than not the data received and described, is never as accurate, never as complete or never as consistent as it is required to be. The result of this would be data flow not being as smooth as intended. When the result isn’t the best the actions and conclusions based on it will not be the best as well, eventually leading to a dissatisfied customer. Some of the common issues encountered as a result of poor quality of data,

• Data duplication – The key reason for this to happen is that systems do not have proper keys (primary or external ids)  i.e. the keys that tie applications.
• Application Crashes – This could happen for many reasons ranging from machine or servers to amount of data transfered.
• Half data being moved – reasons include limitation of data transfered or system governance limits to errors in mapping or data quality or validations
• Performance lag – reasons could be in-adequate resources applied to the integration software to network bandwidth allocated to the integration.
• No synchronization – reasons include in-correct credentials, data mapping errors or data validation issues.

As with any product, with evolving times, comes in more automation targeting performance enhancements and more sophistication, meaning lesser room to ensure data quality. Integration process without accurate, consistent data is as good as integration without the data and eventually business without customers. So it is absolutely necessary to strike the right balance between the two.

Over a period of time we have followed best practices to ensure a smooth integration as any amount of data profiling, data management, data cleansing can ensure at best what can be described as a half solution and not a complete one. Hence we believe in incorporating Customer intelligence into the product and this is being done by gathering key facts about the customer, their nature of business and their data usage and management, a step that not only gives an edge over competition but more importantly also ensures a step ahead in having a satisfied customer.

Do you know how your application is used by your Customers?
Can you predict if a prospect will make a purchase?
Can you identify if your Customer will renew your subscription?

These are some of the common challenges facing every organization. So what is Customer Intelligence?

Customer Intelligence (CI) is the process of gathering and analyzing information regarding customers; their details and their activities, in order to build deeper and more effective customer relationships and improve strategic decision making.

Customer Intelligence is a key component of effective Customer Relationship Management (CRM) such as salesforce.com or Microsoft CRM, and when effectively implemented it is a rich source of insight into the behavior and experience of a company’s customer base.

Customer Intelligence begins with reference data – basic key facts about the customer and their interaction with your company and / or applications. By mining this data, and placing it in context with wider information about competitors, conditions in the industry, and general trends, information can be obtained about customers’ existing and future needs, how they reach decisions, and predictions made about their future behavior.

This data is then supplemented with transactional data – reports of customer activity. This can be commercial information (for example purchase history from sales and order processing), interactions from service contacts over the phone, e-mail, web visits to tracking use of your application. A further subjective dimension can be added, in the form of customer satisfaction surveys or agent data.

This and in my coming series of articles we will address some of the ways to develop strategies and architect solutions and analytics to help you develop a better intelligence set.

Some of the ways to build your customer intelligence

• Tracking Application usage
• Tracking web site activity
• Tracking shopping cart activity and understanding lost baskets
• Integrating Accounting with CRM to track payment patterns and Account Receivables
• Social Media integration and mining
• Predicting Renewals for subscriptions

Developing a strategy for building a Customer Intelligence platform –

Building Customer Intelligence using DBSync

Any Customer Intelligence platform should have the following

• Define: What is that you want from the information and what you plan to do with it
• Model: Define the algorithm that would best define your measures. Measures are the input parameters that you need to have to effectively score and segment your customer. A way to think about it is
  y = f(x1,x2,x3)
  Many times while defining the model we introduce measures which could enhancement and improve your model, but would be difficult to capture given the existing technology and capabilities of the organization. As part of designing CI, efforts should be made to check feasibility of implementing the model.
• Capture: Capture information from your data sources. These could be either from transactional databases or your data warehouse. In general you would have the following setup –
  • Data extraction from applications and datasources into a data ware house
    Example: Extracting web activity from your web application into a data warehouse which could have online customer tracking cookie, IP, referring source, visiting pages, time of visit, length of visit.
  • Aggregating and Summarizing information in data warehouse
    Example: Summarizing data extracted from accounting to track payment patterns and frequency, Credit status or from CRM to look at number of opened cases in last six months.
  • Validate: Run your data captured and summarized to the model developed. Check if your model does provide adequate scoring and segmentation that is required to effectively make decision. A good practice is to segment your score into color codes to define levels of Customer Intelligence Level that could help you quickly say if for example a new Lead is a qualified prospect or an existing customer relationship is going sour.
  • Integrate with CRM: Now that you have valuable information with your model to represent your customer intelligence, you need to integrate and make it easily accessible to your Sales and Marketing team. The best way is to seamlessly integrate with your CRM application so that your marketing team can use your CRM customer or prospect database and Customer Intelligence data along with CRM inbuilt analytics to continue to track and nurture the relationship with your customer.

• • Value— clearly identify information of value.
  • Context— clearly identify the context in which the data was gathered or processed. For instance, an increase in umbrella sales may be due to an increase in local precipitation rather than a fashion trend.
  • Granularity of identity— clearly distinguish and associate between data instances. For example, information surrounding the attributes of customer A may not apply to customer B.
  • Action—The results of analytics should point to a course of action.

Use Case

At DBSync we have a comprehensive customer tracking system built in to track customer usage and satisfaction to build our Customer Intelligence.

We use salesforce.com as our CRM application. Amongst the various models that we track, one is on predicting customer renewals based on usage of our application.

Define: Track and monitor Customer use of DBSync to predict renewals and assist customers continue increase use of the application. Our goal is the have each customer at 7 or higher.

Model: Our model y = f(x1,x2,x3,x4) can be best described as

y = A value between 1 (low usage) to 10 (high usage)
x1 =  Records processed in last 1 week
x2 =  Records processed in last 1 months
x3 =  Records processed in last 6 months
x4 = Date of Customer Acquisition
f(…) = A mathematical weighted model to score the customer.

Capture: Our Extract, Transform and Load for executing this model is as follows –

1. We use DBSync application itself to extract data from our tracking database into our data warehours
2. Use DBSync to execute data warehouse processes to build the data mart for summarization and aggregation.
3. Once our data warehouse is ready we use DBSync to run our Model y =f(x1,..) and populate Salesforce.com customer records.

Integration with CRM: Once information is populated in Salesforce.com, we have reports and dashboards for customer usage analytics. These reports are scheduled to be emailed out every week to the Sales and Management team to track and assist our customers.

Articles and links that would be helpful

Learn More about DBSync

Tutorial on how to use DBSync with database

Register for Free Trial

Integration with Salesforce.com Product Schedules is now available !!!

Lot of customers have been asking us for supporting Product Schedule for recurring invoicing and billing, and now we have the support for it.

Who would benefit from it

• Firms providing Software Subscriptions based products and services.
• Publication firms – magazine, online content, elearning etc
• Services companies providing utilities like waste management, telecom, insurance, day care and others
• Healthcare firms providing on-going services like weight management, home-health and others

How does it work

• Setup your product schedule as you would normally setup in Salesforce. Setup your dates and occurrences
• Setup up scheduler to run every day, week or month. The scheduler provided calculates the next invoice date and updates the next invoice date (custom) field in Opportunity. Use a workflow to update the Generate flag for next invoice.
• You are done!!! DBSync will pickup all invoices as the dates come due and will generate Invoice, Sales Order, Sales Receipt as per Generate Field in opportunity.
• This setup can easily be extended to address most of the business processes around recurring invoice payments.

Installation:

• Login to DBSync
• Click on Library
• Add the process library for Recurring template and use the installation document to install the application.
• Once you install the application, setup the scheduler provided in the installation to match your billing cycle
• As the scheduler runs in the interval assigned, it will mark the corresponding opportunity to generate one of the accounting documents like Invoice, Sales Receipt etc.
• The next time DBSync runs the invoicing process, it will pickup all the Opportunities and process it for invoicing as per setup for Invoice or Sales Receipts.

Please feel free to contact us at support@avankia.com or call 1-877-739-2818 to get more information.

The key problem one of the Radiology – healthcare providers was facing was visibility to their referral information. They were using Net.Orange, and was proving to be quite expensive.

To run their day to day operations, they were using Amicas a leading EMR/Radiology Information System, which managed patient scheduling, physician information and all details other than the billing information, which was managed by another system. While it contained all key data from referral standpoint, they were unable to understand and related referral activities to that of physician liaison and outreach activities.

We used Healthcare CRM: Physician Relationship and Referral Management module built on force.com and were able to quickly setup the sales and marketing end of it. The key still remained to extract information out of the EMR and scheduling system. We had couple of options

Options:

1. Leverage HL7 – HL7 is an event driven protocol and two transactions namely ADT (Admit – discharge) and SIU (Scheduling) was relevant in this case. The issue was that not elements were passed through with the transaction – like complete referring physician information or diagnosis codes and we were not able to extract historical data as it was needed for forecasting future referral.
2. Perform an export in flat-files and manually import it. While it sounded like was in-expensive way to do it, it was quite expensive in the long run. Also, we had seen a drop in usage as folks would forget to perform import.
3. Automate data extract and import of data using DBSync. We were able to tap into the SQL Server and construct views for each of the data sets required for our analysis. We then scheduled data extracts every 15 minutes with the built in scheduler and push to force.com.

Analytics:

While Force.com has built in reports, dashboards and workflows, it does lack some statistical calculations like variance and drop offs. We added real time calculations on a number of statistical calculations required for analysis using APEX – an on-demand programming language for force.com. These routines would run at either real-time to process referral statistics as it came through the integration or run every day at a scheduled interval.

The end result was an on-demand data warehousing and analytical tool to provide clear real-time reports and dashboards, along with workflows to automate notifications of referral drop offs by physicians or new physician referrals.

For more detail check out http://www.avankia.com/healthcarecrm_prm

I am often asked as to what are the relevant reports or metrics we need to track while integrating between Order-to-Cash process. Here is a list of reports that you will find useful while thinking through your business integration needs – mainly between CRM or eCommerce applications like Salesforce.com and Accounting systems like QuickBooks, Intacct or others.

Performance measures for assessment the overall order-to-cash process, order entry-billing, accounts receivable, credit and collections, and inventory accounting.

Order-to-cash

• Total order-to-cash process cost as a percentage of revenue
• Total order-to-cash process cost per order-to-cash FTE
• Days sales outstanding (DSO)
• Operating cycle
• Percentage of order-to-cash key controls that are automated

Order entry – billing

• Total order entry – billing cost as a percentage of revenue
• Total order entry – billing cost per order entry – billing FTE
• Total order entry – billing cost per customer invoice
• Order processing cycle time
• Order-to-fulfillment cycle time
• Order-to-bill cycle time
• Number of days between shipment or service and billing
• Order-to-cash cycle time
• Number of customer invoices per order entry – billing FTE
• Order entry – billing span of control
• Percentage of order entry – billing FTEs in shared services
• Number of separate order entry locations
• Number of separate billing locations
• Number of separate instances of order entry systems
• Number of separate instances of billing systems
• Number of separate customer master files
• Percentage of customers on standard terms

Accounts receivable

• Total accounts receivable subprocess cost as a percentage of revenue
• Total accounts receivable subprocess cost per accounts receivable FTE
• Total accounts receivable subprocess cost per receipt processed
• Total accounts receivable subprocess cost per customer invoice processed
• Average days unapplied cash
• Percentage of customer receipts received electronically
• Methods used for collecting receipts
• Number of receipts per accounts receivable FTE
• Number of customer invoices per receipt
• Percentage of errors in posting receipts
• Average number of days from customer-initiated deduction to resolution
• Accounts receivable span of control
• Percentage of accounts receivable FTEs in shared services
• Number of separate accounts receivable locations
• Number of separate instances of accounts receivable applications

Credit and collections

• Total credit and collections subprocess cost as a percentage of revenue
• Total credit and collections subprocess cost per credit and collections FTE
• Total credit and collections subprocess cost per past-due account
• Total credit and collections subprocess cost per account placed for third-party collection
• Bad-debt expense as a percentage of revenue
• Average days of past-due accounts
• Percentage of invoices paid within bill period- value
• Percentage of invoices paid within bill period- volume
• Average number of days until an invoice would be considered past due
• Average number of days until an invoice would be placed for third-party collection
• Average number of days until an invoice would be written off
• Credit and collections span of control
• Percentage of credit and collections FTEs in shared services
• Number of separate credit and collections locations
• Number of separate instances of credit and collections applications

Inventory accounting

• Total inventory accounting cost as a percentage of revenue
• Obsolescence write-offs as a percentage of revenue
• Annual inventory turnover
• Days inventory on hand (DIO)
• Days to compile year-end inventory
• Number of separate inventory accounting locations