Topic: Customer Behavior

Customer Purchase Probability

Posted by Anonymous on 125 Points
I would like to set up customer purchase probability categories. These would be used in sales forecasting. An example, a Sales rep does a demo with a customer. He rates this customer according to a category. It would one of 3 or 4 catagories. An example of these are;
1. Likely to purchase within 60 days
2. Likely to purchase within 120 days
3. Likely to purchase but time unknown.
4. Unlikely to purchase ever
I would like to get suggestions as to a name for each of the categories, and in our sales forecasting have categories and insert the value of the customers possibly in each of the categories. Any other ideas on handling this is appreciated.
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  • Posted by adammjw on Member

    I really do not think it is important how you call it.The most relevant issue is to have customers ranked properly by your sales reps.If they cannnot rank them well then all the rest is good for nothing.
    As for categories why not call them like:

    1. Hot




  • Posted by Chris Blackman on Member
    Adam's right.

    I'd tend to think of them something like:

    1. Hot prospects
    2. Warm Prospects
    3. Suspects
    4. Non-prospects

    The most important thing is not what you call them, but that you develop a process to migrate these people from unknown status, through suspect to prospect to customer status.

    Why does the sale take so long?

  • Posted by michael on Member
    You can use our PROSPECTRUM(tm) names:

    VOID(not even thinking about your product)
    BREAKTHROUGH (suddenly hits them they might want to consider a product like yours)
    GATHER INFORMATION(collecting info from friends, web, ads etc)
    YES/NO (deciding whether to buy or not...not WHO to buy from)
    ORGANIZING INFO( think of these people as having all info spread out on the table in front of them trying to decide who to buy from)
    RELEASE CASH(red hot and on the way out the door

    This works pretty well for us to see prospects going through stages in the buying process. The problem is that some event can cause a person/company to move very quickly through the stages.

  • Posted by steven.alker on Accepted
    Hi Cheltonak

    The names suggested for your categories present no problems – Chris’ are almost the same as we use.

    Could you let us know how you intend to track these in order to achieve a sales forecast? Is it on a database, a CRM system or manually on a spreadsheet? Also how are you going to arrive at the hot, warm, suspect and no-go ratings? Will it be via a subjective assessment or via some formula relating to availability of budget, authority of the person you made a sales pitch to, how well the demonstration went, fitness of purpose of your product to the application, ROI arguments etc, etc.

    On the forecasting front, there are a few suggestions you might like to consider. Obviously every company is very different, so some of these concepts may not apply to you.

    1. The length of time to close a sale is not necessarily related to the warmth of a sales opportunity. The % likelihood of closing the sale and the actual close date should therefore be separated.

    2. As we are using the term, the “warmth” of a sales opportunity should be calculated from a few simple rules. This is to allow you to achieve consistency in your forecasting over a range of demonstrations, spread out over a period of time. It will also allow you to establish some consistency between different salespeople with wildly different levels of optimism vis-à-vis the likelihood of a sale. The factors involved in this are often:

    Has the company the budget to purchase the product?
    Has the person who has seen the demo got the authority to place an order?
    Does the product match the application?
    Do the benefits of the product translate into a “buy” for the prospect
    Did the demo go well? – did the prospect say yes?
    Are there competitors in the field?
    Was a date for purchase (From whichever vendor is chosen) actually agreed?
    Was the price competitive when asked in the sales interview?

    Quite obviously, these steps and others can all contribute to a % factor of closing a sales, but don’t fall into the common trap of thinking that the factors are all additive. If the answer to the budget question is no or 0%, then even if all the rest of the factors score full marks, the sale will not materialise. Unless you can get around the budget problem – and that’s down to sales skills being deployed beyond the immediate prospect’s authority.

    3. In the sales interview and demonstration, assuming that the prospect was closed on the product, the price and the fitness for purpose, the salesman would then ask “When do you intend to place an order” I would hope that he would ask for the business there and then, but the reality of the situation is that the order will often be promised at a future date. This leads you into your 60 days, 120 days, unknown etc.

    These are very broad bands and not much use in forecasting. Having an opportunity with 60 days or 120 days attached to it makes forecasting rather difficult as you are always having to re-base your actual forecasts from the date of the demonstration.

    It is much better that they simply ask the question “When do you expect to place an order with us” and note the response. Qualify it by asking if he may call the prospect closer to that date and book the confirmatory call in his diary. The proffered date, rather than the duration of the demo to order cycle is much more useful. You can always calculate the duration from the proffered date and band it into whatever number of days you want for reporting purposes.

    4. By this stage you will now have a number of sales opportunities and each will have the following attributes to allow you to make a forecast:

    % Likelihood of closing the deal
    Date of securing the order
    Monetary Value of the order.

    Now you need to see how the beast changes over time!

    5. Follow-up calls between the demonstration and the promised order are essential to tracking an order and to see if anything has changed which might influence the success of the sale. The forecast therefore will need to be adjusted as the sales executive becomes aware of new, relevant information. A competitor might be kicked out of the process. The buyer might change. The budget might go up or down. And so on.

    6. As a result of follow up activity, the forecast will be dynamic. This is very difficult with fixed “Days to order” figures. Any of the factors mentioned above can change the timing and likelihood of an order and the changed situation needs to be reflected in the forecast.

    That’s not a reason to chuck away the original forecast. The very best sales forecasting systems incorporate all these ideas and keep a snapshot of every change to every forecasted sale. Checking the trends on the forecasts will tell you a lot about the work a sales person does to keep a prospective order on course and also allow you to understand the relative levels of optimism and pessimism being exhibited by different sales people in different deals.

    The final comparison is the forecast against the order book. When the promised dates arrive, it is essential to be able to compare sales forecast with sales reality, explain the difference, analyse what has happened and see if there are lessons to be learned or remedial actions that can be taken to rescue a lost or slipping sale.

    7. The last point, if applied with some degree of rigour will allow management and sales people to identify sales which are showing signs of being in danger and more importantly, identify measures which can be taken to rescue them.

    I guess that this is a bit more complicated than what you had in mind, but it needn’t be! A decent CRM system will look after leads and opportunities according to the rules that you set. It will produce the reports and forecasts you want without any onerous data entry or data manipulation and it will tell you with a high level of accuracy and consistency what your sales figures are likely to be for as far ahead as your order cycle permits.

    I’d like to make one last point. Agreeing to what constitutes a sales forecast actually is one of the hardest definitions a management might have to agree on. Sales, Marketing, Production, Purchasing and Finance will all have different perspectives, imperatives and different priorities for seeing the figures in such-and-such a light. I have seen boards of directors nearly come to blows because I have asked them valid questions to which they all had the right answers. Unfortunately they were all different answers to the same question!

    For your information, we make Maximizer Enterprise do the above steps. We utilise it’s inbuilt lead and opportunity management features to do the majority of the work, some add-on’s to create snap-shots of a dynamic sales forecasting environment and it’s built-in Crystal Reports software to generate something for management and sales staff to read, analyse and act on.

    Here endeth the lesson!

    Steve Alker
    Unimax Solutions

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