Making Click-and-Collect click

In my previous post, I introduced you to integrated e-commerce and explained why it is the right way to extend your business online. If you already have a brick-and-mortar retailing business and you’re looking to improve your online presence, you could start offering a click-and-collect service.

With click-and-collect, customers order online and then collect their merchandise from one of the retailer?s local branches. Why would they want to do that?

Apparently, there are buyers who now prefer a click-and-collect service over the delivery service of a purely online retailer. With the latter, they sometimes have to wait forever for the delivery van to arrive or contend with a missed-delivery card.

Basically, customers who want both the convenience of placing orders online and better control of their time find click-and-collect a better option.

Last December 2011, IMRG (Interactive Media in Retail Group) reported a ?significant rise in the percentage of click-and-collect e-retail sales in the 3rd quarter of 2011?. This accounted for 10.4% of all e-retail sales in that quarter. More specifically, the gain was 7.4%, which was also the strongest quarterly gain since IMRG started collecting this data.

Clearly, this particular service is gaining popularity. But how do you meet the rising demand in this area?

A click-and-collect service requires a highly synchronised ecosystem. You don’t want to have a customer order items from your online store, drive a couple of minutes from his house to your nearest outlet, only to find out that one of the items is no longer available.

This can only work if all systems involved are interconnected. Changes in the inventory in your individual outlets should reflect on your database in real time. In turn, these changes have to be reflected instantly on your online store. Conversely, once a buyer has picked items online and is already directed to a local outlet, those items have to be reserved there.

But that’s not all. Your system has to be seamless enough to support fast and reliable service. You don’t want your buyer to have to wait a long time before the items are ready for pick-up. It also has to be capable of tracking the status of ordered products, handling uncollected orders, and monitoring inventory.

By implementing an integrated e-commerce system, these won’t be the only things you?d be able to do. You can even add more value to your service. For example, you can connect to your CRM and learn more about your customers? purchase history, buying habits, and preferences.

That way, it would be easier for you to provide a faster and more convenient buying experience for them in the future.

Click-and-collect is a very promising way to increase your sales and improve customer loyalty.

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A Business Case for Sharing

We blogged about sharing services in a decentralised business context recently, and explained why we think why these should be IT-Based for speedy delivery. This is not to say that all shared services projects worldwide have been resounding successes. This is often down to the lack of a solid business case up front. We decided to lay out the logic behind this process.

Management Overview ? The overview includes a clear definition of why the current situation is unacceptable, the anticipated benefits of sharing, and an implementation plan were it to go ahead. The project should not proceed until the stakeholders have considered and agreed on this.

Alternatives Considered ? The next stage is to get closer to the other options in order to determine whether an alternative might perhaps be preferable. Substitutes for shared services are often doing nothing, improving the current method, and outsourcing the service to a third party.

The Bottom Line in Business ? Sharing services comes at an initial cost of infrastructure changes, and the impact on human capital (the latter deserves its own blog). The following need careful consideration from the financial angle:

Numbers to Work Through

  • Manpower to design and roll the project out in parallel with the existing organisation.
  • Capital for creating facilities at the central point including civil works, furniture and equipment and IT infrastructure.
  • The costs of travel, feeding and accommodation. These can be significant depending on the time that implementation takes.
  • The opportunity loss of diverting key staff – and the cost of temporary replacements – if appointing line staff to the project team.
  • Crystal-clear project metrics including (a) the direct, realisable savings (b) the medium and long-term effects on profit and (c) where to deploy the savings

Risk Management

Shared services projects don’t go equally smoothly, although planning should reduce the risk to manageable levels. Nonetheless it is important to imagine potential snags, decide how to mitigate them and what the cost might be.

We believe in implementing shared services on a pilot basis in the business unit that eventually provides them. We recommend building these out to other branches only when new processes are working smoothly.

Moving On From a Decision

We recommend you revisit your management overview, the logic behind it, the assumptions you made, and the costs and benefits you envisage before deciding to go ahead

The final step in proving a business case is doable should be fleshing out your roadmap into a detailed operations plan with dependencies on a spreadsheet.

The Better Way of Applying Benford’s Law for Fraud Detection

Applying Benford’s Law on large collections of data is an effective way of detecting fraud. In this article, we?ll introduce you to Benford’s Law, talk about how auditors are employing it in fraud detection, and introduce you to a more effective way of integrating it into an IT solution.

Benford’s Law in a nutshell

Benford’s Law states that certain data sets – including certain accounting numbers – exhibit a non-uniform distribution of first digits. Simply put, if you gather all the first digits (e.g. 8 is the first digit of ?814 and 1 is the first digit of ?1768) of all the numbers that make up one of these data sets, the smallest digits will appear more frequently than the larger ones.

That is, according to Benford’s Law,

1 should comprise roughly 30.1% of all first digits;
2 should be 17.6%;
3 should be 12.5%;
4 should be 9.7%, and so on.

Notice that the 1s (ones) occur far more frequently than the rest. Those who are not familiar with Benford’s Law tend to assume that all digits should be distributed uniformly. So when fraudulent individuals tinker with accounting data, they may end up putting in more 9s or 8s than there actually should be.

Once an accounting data set is found to show a large deviation from this distribution, then auditors move in to make a closer inspection.

Benford’s Law spreadsheets and templates

Because Benford’s Law has been proven to be effective in discovering unnaturally-behaving data sets (such as those manipulated by fraudsters), many auditors have created simple software solutions that apply this law. Most of these solutions, owing to the fact that a large majority of accounting departments use spreadsheets, come in the form of spreadsheet templates.

You can easily find free downloadable spreadsheet templates that apply Benford’s Law as well as simple How-To articles that can help you to implement the law on your own existing spreadsheets. Just Google “Benford’s law template” or “Benford’s law spreadsheet”.

I suggest you try out some of them yourself to get a feel on how they work.

The problem with Benford’s Law when used on spreadsheets

There’s actually another reason why I wanted you to try those spreadsheet templates and How-To’s yourself. I wanted you to see how susceptible these solutions are to trivial errors. Whenever you work on these spreadsheet templates – or your own spreadsheets for that matter – when implementing Benford’s Law, you can commit mistakes when copy-pasting values, specifying ranges, entering formulas, and so on.

Furthermore, some of the data might be located in different spreadsheets, which can likewise by found in different departments and have to be emailed for consolidation. The departments who own this data will have to extract the needed data from their own spreadsheets, transfer them to another spreadsheet, and send them to the person in-charge of consolidation.

These activities can introduce errors as well. That’s why we think that, while Benford’s Law can be an effective tool for detecting fraud, spreadsheet-based working environments can taint the entire fraud detection process.

There?s actually a better IT solution where you can use Benford’s Law.

Why a server-based solution works better

In order to apply Benford’s Law more effectively, you need to use it in an environment that implements better controls than what spreadsheets can offer. What we propose is a server-based system.

In a server-based system, your data is placed in a secure database. People who want to input data or access existing data will have to go through access controls such as login procedures. These systems also have features that log access history so that you can trace who accessed which and when.

If Benford’s Law is integrated into such a system, there would be no need for any error-prone copy-pasting activities because all the data is stored in one place. Thus, fraud detection initiatives can be much faster and more reliable.

You can get more information on this site regarding the disadvantages of spreadsheets. We can also tell you more about the advantages of server application solutions.

Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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