Which KPI?s to Use in CRM

Customer relationship management emerged in the 1980?s in the form of database marketing. In those tranquil pre-social media days, the possibility of ?managing? clients may have been a possibility although Twitter and Facebook took care of that. Modern managers face a more dynamic environment. If you are one, then what are the trends you should be monitoring yourself (as opposed to leaving it to others).

If you want to drip feed plants, you have to keep the flow of liquid regular. The same applies to drip-feed marketing. Customers are fickle dare we say forgetful. Denizon recommends you monitor each department in terms of Relationship Freshness. When were the people on your list last contacted, and what ensued from this?

Next up comes the Quality of Engagements that follow from these efforts. How often do your leads respond at all, and how many interfaces does it take to coax them into a decision? You need to relate this to response blocks and unsubscribes. After a while you will recognise the tipping point where it is pointless to continue.

Response Times relate closely to this. If your marketing people are hot then they should get a fast response to sales calls, email shots and live chats. It is essential to get back to the lead again as soon as possible. You are not the only company your customers are speaking too. Fortune belongs to the fast and fearless.

The purpose of marketing is to achieve Conversions, not generate data for the sake of it. You are paying for these interactions and should be getting more than page views. You need to drill down by department on this one too. If one team is outperforming another consider investing in interactive training.

Finally Funnel Drop-Off Rate. Funnel analysis identifies the points at which fish fall off the hook and seeks to understand why this is happening. If people click your links, make enquiries and then drift away, you have a different set of issues as opposed to if they do not respond at all.

You should be able to pull most of this information off your CRM system if it is half-decent, although you may need to trigger a few options and re orientate reporting by your people in the field. When you have your big data lined up speak to us. We have a range of data analysts brimming over with fresh ideas.

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Energy Cooperation Mechanisms in the EU

While the original mission of the European Union was to bring countries together to prevent future wars, this has spun out into a variety of other cooperative mechanisms its founders may never have dreamed of. Take energy for example, where the European Energy Directive puts energy cooperation mechanisms in place to help member states achieve the collective goal.

This inter-connectivity is essential because countries have different opportunities. For example, some may easily meet their renewable targets with an abundance of suitable rivers, while others may have a more regular supply of sunshine. To capitalise on these opportunities the EU created an internal energy market to make it easier for countries to work together and achieve their goals in cost-effective ways. The three major mechanisms are

  • Joint Projects
  • Statistical Transfers
  • Joint Support Schemes

Joint Projects

The simplest form is where two member states co-fund a power generation, heating or cooling scheme and share the benefits. This could be anything from a hydro project on their common border to co-developing bio-fuel technology. They do not necessarily share the benefits, but they do share the renewable energy credits that flow from it.

An EU country may also enter into a joint project with a non-EU nation, and claim a portion of the credit, provided the project generates electricity and this physically flows into the union.

Statistical Transfers

A statistical transfer occurs when one member state has an abundance of renewable energy opportunities such that it can readily meet its targets, and has surplus credits it wishes to exchange for cash. It ?sells? these through the EU accounting system to a country willing to pay for the assistance.

This aspect of the cooperative mechanism provides an incentive for member states to exceed their targets. It also controls costs, because the receiver has the opportunity to avoid more expensive capital outlays.

Joint Support Schemes

In the case of joint support schemes, two or more member countries combine efforts to encourage renewable energy / heating / cooling systems in their respective territories. This concept is not yet fully explored. It might for example include common feed-in tariffs / premiums or common certificate trading and quota systems.

Conclusion

A common thread runs through these three cooperative mechanisms and there are close interlinks. The question in ecoVaro?s mind is the extent to which the system will evolve from statistical support systems, towards full open engagement.

Spreadsheet Risk Issues

It is interesting to note that the riskiness of operational spreadsheets are overlooked even by companies with high standards of risk management. Only when errors amount to actual losses do they realize that these risks have been staring them in the face all along.

Common spreadsheet risk issues

Susceptibility to trivial manual errors

Due to the fundamental structure of spreadsheets, a slight change in the formula or value in any of their inhabited cells may already affect their overall output. An

  • accidental copy-paste,
  • omission of a negative sign,
  • erroneous range selection,
  • incorrect data input or
  • unintentional deletion of a character,cell, range, column, or row

are just some of the simple errors spreadsheet users frequently encounter. Rarely are there any counter-checking controls in place in a spreadsheet-based activity and manual errors therefore easily go undetected.

Possibility of the user working on the wrong version

How do you store spreadsheet files?

Since the most common reports are usually generated on a monthly basis, users tend to store them using variations of these two configurations:

spreadsheet storage

If you notice, a user can accidentally work on the wrong version with any of these structures.

Prone to inconsistent company-wide reporting

This happens when a summary or ?final? spreadsheet is fed information by different departments coming from their own spreadsheets. Even if most of the data in their spreadsheets come from one source (the company-wide database), erroneous copy-pasting and linking, or even different interpretations of the same data can result to contradicting information in the end.

Often defenceless against unauthorised access

Some spreadsheets contain information needed by various individuals or department units in an organisation. Hence, they are often shared via email or through shared folders in a network. Now, because spreadsheets don’t normally use any access control, any user can easily open a spreadsheet file and view or modify the contents as he wishes.

Highly vulnerable to fraud

A complex spreadsheet system with zero or very minimal controls provides the perfect setting for would-be fraudsters. Hidden cells with malicious formulas and links to bogus information can go unnoticed for a long time especially if the final figures don’t deviate much from expected values.

Spreadsheet risk mitigation solutions may not suffice

Inherent complexity makes testing and logic inspection very time consuming

Deep testing can uncover possible errors hidden in spreadsheet cells and consequently mitigate risks. But spreadsheets used to support financial reporting are normally large, complex, highly-personalised and, without ample supporting documentation, understandably hard to follow.

No clear ownership of risk management responsibilities

There?s always a dilemma when an organisation starts assigning risk management responsibilities for spreadsheets. IT personnel believe users in the business side of the organisation should be responsible since they are the ones who create, edit, store, duplicate, and share the spreadsheet files. On the other hand, users believe IT should be responsible since they have always been in-charge of managing IT infrastructure, applications, and files.

To get rid of spreadsheet risks, you’ll have to get rid of spreadsheets altogether

One remedy is to have a risk management activity that involves both IT personnel and spreadsheet users. But wouldn’t you want to get rid of the complexity of having to distribute the responsibilities between the two parties instead of just one?

Learn more about Denizon’s server application solutions and how you can get rid of spreadsheet risk issues.

More Spreadsheet Blogs


Spreadsheet Risks in Banks


Top 10 Disadvantages of Spreadsheets


Disadvantages of Spreadsheets – obstacles to compliance in the Healthcare Industry


How Internal Auditors can win the War against Spreadsheet Fraud


Spreadsheet Reporting – No Room in your company in an age of Business Intelligence


Still looking for a Way to Consolidate Excel Spreadsheets?


Disadvantages of Spreadsheets


Spreadsheet woes – ill equipped for an Agile Business Environment


Spreadsheet Fraud


Spreadsheet Woes – Limited features for easy adoption of a control framework


Spreadsheet woes – Burden in SOX Compliance and other Regulations


Spreadsheet Risk Issues


Server Application Solutions – Don’t let Spreadsheets hold your Business back


Why Spreadsheets can send the pillars of Solvency II crashing down

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Uncover hidden opportunities with energy data analytics

What springs to mind when you hear the words energy data analytics? To me, I feel like energy data analytics is not my thing. Energy data analytics, however, is of great importance to any organisation or business that wants to run more efficiently, reduce costs, and increase productivity. Energy efficiency is one of the best ways to accomplish these goals.

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Using energy data analytics can enable an organisation to: determine discrepancies between baseline and actual energy data; benchmark and compare previous performance with actual energy usage. Energy data analytics also help businesses and organisations determine whether or not their Building Management System (BMS) is operating efficiently and hitting the targeted energy usage goals. They can then use this data to investigate areas for improvement or energy efficient upgrades. When energy data analytics are closely monitored, companies tend to operate more efficiently and with better control over relevant BMS data.

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