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Call Forecasting

  • This topic has 18 replies, 1 voice, and was last updated 19 years ago by Tapoti.
Viewing 15 posts - 1 through 15 (of 19 total)
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  • #11319 Reply
    Adinugroho
    Guest

    I’m doing a reseach about call center operator scheduling. But I’m having trouble in predicting the traffic (incoming call) Can you help me ?

    Thank you very much

    Adinugroho
    adinugie@apexmail.com

    #11320 Reply
    Bhavesh Vora
    Guest

    There are certain commercial Forecasting Software like Lucent’s Forecasting Utility along with Center Vu CMS.
    However I do not know of any shareware tools. If you need more info about the Lucent tools let me know.

    Bhavesh

    bkvora@rediffmail.com

    #11321 Reply
    emma
    Guest

    i am also trying to find out the same kind of info, if you could let me know your results i would be very grateful.

    cheers
    emma

    #11322 Reply
    Sheila
    Guest

    Hello Adi.

    Have you found a solution to your question yet? Appreciate you sharing the info. I’m stuck on the call volume projection. We’re in the service industry but since we are not operational yet, there is no history data to begin with.

    #11323 Reply
    Vijay Swami
    Guest

    Call volume forecasting methods can be very simple like using a regression method or could be very complex (like using Box Jenkins, or using complex seasonality algorithms etc)
    However the very simple set of methods seem to be very powerfully working for me
    You may consider doing this simple method
    WHEN YOU HAVE DATA:
    1. Take recent two years worth of data (please make sure that the business context has not changed in that time of data you are selecting. For example if there is a huge system change like a cut over of new technology, having this data will bias heavily)
    2. Observe a typical week and find out the day for which the call volumes peak. For example Mondays may be the typical week you will see this high volume
    3. After doing that take each month and calculate how many days are there in that particular month. For example 2000-Aug may have 4 sundays, 4 mondays etc
    4. Now express the months in terms of the heaviest call volume day. For example 2000-Aug may have call volumes corresponding to 30 typical mondays and so on
    5. After you have that data find the factor calls received/Equivalent day factor
    6. Once you have those set of values for the two years do a linera Regression to find out the Slope, and the Constant
    in the equation Y = mx+C
    7. If you slove this equaltion and obtain m and C you can use that for future projections
    Amazingly this simple model has worked out very well
    But again, this may not work in all the situation

    WHEN THERE IS NO DATA
    I have been in this situation once
    What I did was the following
    1. I took the business logic of the product
    2. In this case it was an airline company
    3. To the call I mapped out the list of all the activities that could possibly lead the generation of the call
    4. For eaxmple if its an airline help desk
    —Passengers may call
    —employees within the comapny may call
    –If there is any software in the Airport those technical people may call
    like this we listed out all the factors
    This may appear simple but we have to carefully think as there is no data
    4. Now we attached a probability for each factor. What is the probabilty of having X number of calls in one half hour from a passenger (This will be influenced by having a promotion, Ad time in the TV etc etc)
    5. Now you get set of probability with a set of calls associated with each factor categorised into each sub factors
    You can do an additive model from that point to estimate the call volume

    If any one would like further clarifications (as these are pretty lengthy stuff and I dont know how much I have explained) please feel free to email at
    vijay_swami@yahoo.com

    Thanks
    -Vijay

    #11324 Reply
    Surya
    Guest

    That was great Vijay, but do you have any other method or process through which call traffic could be predicted.

    If you have any please let me know, you can mail me at sury@servingminds.com

    #11325 Reply
    Arunabh Mazumdar
    Guest

    Hi Vijay,
    The research work done by you is fabulous.
    But do you really think that a pure statistical approach is the way to forecasting.
    Surely, probability also plays a vital role.
    That apart it is equally important to understand the process involved with each call.
    The demography of the market one is serving.
    Hope we arrive at a common model which is simple and can be used as a thumb rule by all regardless of the type of calls one manages.
    Regards,
    Arunabh.

    #11326 Reply
    Jasen Shirley
    Guest

    I think all of you have some outstanding ideas. For someone that is just really getting into the call center game, this information is really great. I’ll keep watching and maybe I can share some thoughts along the way.

    #11327 Reply
    Mike Knight
    Guest

    Does anyone know where I can find a statistic that states how many callers abandon and retry (into the call center) Thanks,

    #11328 Reply
    Vijay
    Guest

    Hi Mike,
    I am not sure if I understand the question. But things like Abandon rate will be available through the carrier reports that can be obtained by calling the carrier for the call center (Like AT&T, MCI, etc)
    If you are looking for a specfic industry wide standards though its difficult to look for all the specific industries, tons of valuable info is available in
    http://www.benchmarkportal.com

    Thanks
    -Vijay

    #11329 Reply
    Jan Kalden
    Guest

    just a few ideas,

    You can predict the volume that will come in. There are many applied math. procedures to predict the number of incoming calls. e.i. Arma, Holt-Winters, seasonal HW, etc. But you always have to make caleder adjustments.
    You need 3 years of data to have a workable medium for the calender adjustments. You can use this data in order to improve your prediction model. Try past data in order to see what the result is yoy model (use the best fit method in order to diagnose the model. You can have more models at the same time. You have to weight the models. ax+by+cz, and the factor must be able to change ongoing.

    I can tell you more but my battery is low.

    regards jan kalden

    #11330 Reply
    Jonathan
    Guest

    I work for a fortune 500 company, and my position is in forecasting and strategies. I have reviewed the various forecasting techniques for call centers and I agree as well as incorporate most of the practices. The problem I have come across is how to calculate an accurate staff required based on the normal parameters (i.e. service level, handle time, etc). I am currently using the Erlang C calculation, but I am not 100% confident with it. Is there any other methodologies anyone has found to be successful?

    Thanks, Jonathan

    #11331 Reply
    jan kalden
    Guest

    I’m convinced hat one has to predict the external items first before one can forecast the internal processen like call center data. This forecast can be used in the Ehrlang b and c models. However this is indeed not sufficient. You can use a linear model in order to predict the operationel and staf agents per hour. If interested jankalden@hetnet.nl

    #11332 Reply
    Vijay
    Guest

    I am sorry to use this forum for this. But please forgive me!
    I just got laid off because of a Reduction in Workforce in my company. I am intersted in taking any call center position
    Can anyone help?
    Thanks a lot
    -Vijay

    #11333 Reply
    Marco Anunciação
    Guest

    Vijay; I hope what he may have encountered new position in Call Center, you wed no mail me:marcoanunciacao@yahoo.com_.

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