Kevel Forecast provides the insights you need to see what inventory is available and how a campaign will deliver. You have the ability to predict future ad traffic levels and campaign inventory availability using an unlimited number of targeting variables, including geo, keyword, key-value, user segment, or frequency capping.

For Forecast use cases and types, check out the Forecast Overview.

Getting Started

Asynchronous API

It’s important to note that the Kevel Forecast API is asynchronous. You can request a forecast to be performed via a POST to https://api.kevel.co/v1/forecaster/ with the appropriate API key and you always get back a response such as the following:

{
  "id": "8a6f6bf5-f823-409f-8746-9ae047190cc3",
  "status": "enqueued",
  "progress": 0.0,
  "desc": "Forecast is queued"
}

This object contains an id that you can use in a GET request to https://api.kevel.co/v1/forecaster/{id} to get the forecast result back. Forecasts can take a few seconds to some minutes to complete, depending on the amount of ads involved, how far into the future we’re forecasting and the sampling level. A complete forecast result is one whose response has the status “finished”. For example, the following is a response to a finished forecast:

{
  "resultStatus": "success",
  "result": {
    "total": {
      "329797406": {
        "impressions": 0,
        "uniqueUsers": 0
      },
      "336416380": {
        "impressions": 76792,
        "uniqueUsers": 31718
      }
    }
  },
  "id": "8a6f6bf5-f823-409f-8746-9ae047190cc3",
  "status": "finished",
  "progress": 100.0,
  "desc": "Forecast is finished"
}

Forecast dates

Forecast requests require an end date and, optionally, in some forecast types, the start date as parameters. These dates can suffer minor adjustments to reflect the availability of future inventory and existing campaigns' information.
The dates effectively used by the forecast are returned in its results as forecastStartDate and forecastEndDate.

Grouping-by and time zone considerations

In GroupBy you can define any key you can use in custom targeting, as well as some custom ones like $datetime.date. For example, the following payload triggers a forecast request for existing ads using sampling level 3 and grouping results by site and date:

{
  "Type": "existing",
  "EndDate": "2023-03-20",
  "Params": {
    "Sampling": 3,
    "GroupBy": ["$site", "$datetime.date"],
    "TimeZone": "UTC"
  }
}

Forecast results for requests with a GroupBy parameter have an additional field in the result object, called grouped, where you can see results for each ad grouped by the possible values of each tuple of grouping keys. The TimeZone parameter in the Params field specifies in which time zone should time-related fields in the GroupBy be considered in, defaulting to UTC if no TimeZone is set.

{
  "resultStatus": "success",
  "result": {
    "total": {
      "1234567891": {
        "impressions": 0,
        "uniqueUsers": 0
      },
      "1234567890": {
        "impressions": 76136,
        "uniqueUsers": 31680
      },
      "1234567899": {
        "impressions": 294776,
        "uniqueUsers": 97152
      },
      (...)
    },
    "grouped": [
      {
        "key": {
          "$site": 1234567,
          "$datetime.date": "2023-03-14"
        },
        "values": {
          "1234567891": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          "1234567890": {
            "impressions": 38488,
            "uniqueUsers": 17464
          },
          "1234567899": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          (...)
        }
      },
      {
        "key": {
          "$site": 1234567,
          "$datetime.date": "2023-03-14"
        },
        "values": {
          "1234567891": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          "1234567890": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          "1234567899": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          (...)
        }
      },
      {
        "key": {
          "$site": 1234568,
          "$datetime.date": "2023-03-14"
        },
        "values": {
          "1234567891": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          "1234567890": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          "1234567899": {
            "impressions": 0,
            "uniqueUsers": 0
          },
          (...)
        }
      },
      (...)
    ]
  },
  "id": "33665016-a0e6-4bc0-98a2-ad14aca86053",
  "status": "finished",
  "progress": 100.0,
  "desc": "Forecast is finished"
}

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Large result set warning

Note that large combinations of grouping keys can result in very large forecast results. Settings within the Params object can be used with any forecast type.
When no value is available for the specified GroupBy key in the predicted impression, the results will be grouped by the value null for that key.

Group-by array fields

When the group-by field is an array, and not individual values, the default behavior is for results to be shown for the set of values and not for each individual value. For example, it is common for a single ad opportunity to have multiple AdTypes or Keywords. A group-by $keywords results in a group for each set of keywords, e.g. forecasted metrics for ad opportunities with all three keywords: ["bunny","business","tacocat"]will be presented as a single group-by dimension. This behavior ensures that you can sum all the individual group-by ad opportunities forecasted and they will add up to the total of ad opportunities.

But sometimes it's desirable to break down by individual keyword or ad type. Forecast supports this by having the ability to parse out any array field by appending the suffix []to the field name, e.g. $keywords[] or $adTypes[]. To make it simpler, it also supports certain aliases for known standard fields, namely $keywordin place of $keywords[] and $adType in place of $adTypes[]. See the details in the table for all available aliases in Forecast.

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You cannot sum individual results of a group-by individual array elements

The sum of all individual results in a group by when using array fields individual elements will not add up to the total results. For example, there is 1 ad impression available for keywords ["bunny","business","tacocat"]and when broken down by $keyword they will show 1 impression each but the total impressions is still just 1, and if one ad is booked against any of the three keywords it will remove availability for the other two.

Group-by fields

The following keys are available for use in the GroupBy parameter:

Multi-winner scenarios

In the Kevel Ad Platform you can make a single request and get multiple ads back (multi-winner placements). The actual availability and deliverability of ads depend on what you do with each individual ad selected in a multi-winner ad request. By default, the Forecast assumes all ads in will be used/delivered from each list of multi-winner ads, but you can adjust that default behavior at the network level by contacting your Customer Success manager, and experiment with different settings at each forecast request by using the other options below:

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Caution: forecasts that use a non-default multi-winner setting will take longer to run

Dynamic winner ratio

Setting the DynamicMultiWinnerImpressionRatio to true uses the previously observed winners to delivered impressions ratio for each different ad request. So if the number of delivered impressions is different for each of your placements, sites or other dimensions, this setting is likely what you'd like to use as it will simulate the delivery of a variable number of ads for each future ad request - even if you're always requesting the same number of winners. When used, it takes precedence over the static MultiWinnerImpressionRatio if both are set.

Static winner ratio

If you want to simulate a single winner ratio across all ad requests, use the MultiWinnerImpressionRatio parameter in the Forecast request which represents the percentage (from 0 to 1) of ads you expect to deliver from the list of returned winners. The ratio is applied to the Placement's count and rounded up to the nearest value. The resulting value is clamped between 1 and the placement's count. e.g., for a ratio of 0, the maximum number of opportunities to consider for that placement will be 1. If you always deliver all ads sent back in a multi-winner response, you would use the ratio of 1. If you tend to select for delivery 5 out of 10 you would set a ratio of 0.5.

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Additional Display Rules

It's common for Additional Display Rules to be used alongside multi-winner placements, these are also taken into account when forecasting multi-winner scenarios (and other multi-request scenarios such as multiple placements on the same page).

Choosing the right winner ratio setting for your network

Quick assessment questions:

“Of all ad decision requests and their individual ad counts, which do you want to consider to be real opportunities?”.
Another way of asking the same question is:
“How do you handle the ad selections Kevel sends back in each ad decision request? Will you always serve all ads sent back or will you do any post-filtering?”

If the answer is:

1. All winners requested on all ad decision requests are an opportunity (i.e. all are served): ratio of 1; This is the standard and default.

2. Each Ad Decision Request will deliver exactly one ad, regardless of how many winners are requested: ratio of 0;

3. Each Ad Decision Request requests multiple ads and the number of ads actually delivered are a fixed percentage of the ads requested: ratio from 0 to 1 representing the % (e.g. 0.5=50% are delivered opportunities). E.g. request 10 winners and serve 5 of them.

4. The number of ads served varies and the percentage of ads served from those returned in the response is not always the same. And/or the delivery is only considered effective when ads are seen (i.e. triggered only on view-ability). In this case, the best option is to use DynamicMultiWinnerImpressionRatio but it is important to ensure that there are always impressions fired for ad opportunities that need to be considered, i.e. there shouldn't be Blank responses for ad request, because in this case all blank responses will not be considered as possible inventory in the future. One strategy is to create un-targeted, house-level ads without any limits or caps, that will always serve in case there aren’t any paid ads for those spaces.

Click forecasts

Forecast responses also return the estimated number of Clicks, alongside the Impression and Unique Users. This enables CTR can be easily derived (Clicks / Impressions) for any resulting forecast at any grouping level (e.g. CTR by Site, Geo and Placement):

{
    "requestDateTime": "2023-09-22T14:14:08.540Z",
    "lastUpdateDateTime": "2023-09-22T14:14:15.070Z",
    "startDateTime": "2023-09-22T14:14:09.176Z",
    "endDateTime": "2023-09-22T14:14:15.070Z",
    "resultStatus": "success",
    "result": {
        "total": {
            "clicks": 880,
            "impressions": 135392,
            "uniqueUsers": 43952
        },
        "grouped": [
            {
                "key": {
                    "$site": 12345679
                },
                "value": {
                    "clicks": 0,
                    "impressions": 168,
                    "uniqueUsers": 24
                }
            },
...
        ]
    },
    "id": "45dff65f-e4a0-4f8a-98b9-960a9f5b7240",
    "status": "finished",
    "progress": 100.0,
    "desc": "Forecast is finished"
}

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Lag between impression and click event tracking

A drift/delay in sending Click events may result in missed and inaccurate click predictions, so it is advised to minimize the delay between a click happening and sending it to Kevel.