At a glance
Ranked list of the merchant’s top-performing campaigns and programmes by purchase lag, the median time between a Dotdigital send (or click) and the resulting placed-order event. Reveals which sends drive immediate purchase (“flash” cadence) vs which build pipeline that converts later (“nurture” cadence). The card surfaces the campaigns that are most worth replicating and the cadences that are silently building backlog.
Calculation
Calculated automatically from your Dotdigital data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A UK DTC homewares brand on BigCommerce + Dotdigital, mature email programme. Window covers 12 Jan 26 to 12 Apr 26 (90 days).
What’s interesting:
- Abandoned-Cart Step 1 has the shortest lag (2.4 hours) and the highest recovered revenue. This is the merchant’s most valuable single send, every minute the programme is paused costs roughly £15 in lost recovery. The median of 2.4 hours means most recoveries happen within the same browsing session.
- Win-back lag is 48.2 hours, much longer than transactional triggers. That’s expected behaviour for a win-back send to dormant contacts; they need time to re-engage. The merchant should not optimise for shorter win-back lag (it would damage open rate); they should optimise for win-back conversion rate over the 7-day window.
- Mother’s Day campaign showed an 8.1-hour median lag, longer than abandoned cart but shorter than nurture programmes. This is a classic tentpole pattern: the email lands, customers browse, decide overnight, and most place orders the next morning. Compare against last year’s Mother’s Day campaign (if available) to see whether lag has shortened (better targeting, faster intent capture) or lengthened (more promotional fatigue, longer consideration).
- Post-purchase upsell Step 3 has 72.4-hour lag but generates £4,800. This is the “slow burn” tier of the programme, customers come back days later, often after using the original product. A merchant who pauses Step 3 to “trim” the programme will see no immediate revenue impact but lose £4,800 / 90 days = ~£1,600 / month over time.
- Browse Abandonment lag (6.8 hours) is faster than the campaign average. Browse abandonment fires earlier in the funnel than cart abandonment, but the customer has demonstrated less commitment. The shorter lag tells you the programme is hitting a high-intent population, every minute it’s paused costs revenue.
Sibling cards merchants should reference together
Reconciling against the vendor’s own dashboard
Where to look in Dotdigital: Dotdigital does not expose a “purchase lag” view natively. The closest screens for cross-checking the inputs:- r1-app.dotdigital.com → Insights → Programme Performance for per-programme attributed revenue and conversion-window settings.
- r1-app.dotdigital.com → Campaigns → individual campaign report for per-campaign send-to-conversion timestamps in the activity log.
- r1-app.dotdigital.com → Reports → Custom Reports can be configured to show
Placed Order timestamp - Send timestampper recipient, the manual reconstruction of this card.
Cross-connector reconciliation:
The most common reconciliation question is “why does my Dotdigital reported attributed revenue not match the sum of recovered revenue across these top sends?” Three causes: (a) the top-N truncation excludes the long tail (15 to 30% of attributed revenue typically), (b) some attributed orders cannot be matched to a specific send (assigned to “general programme” or “campaign rollup”), (c) refund-window timing.
Known limitations / merchant FAQs
Why does my abandoned-cart programme show up at the top? Because abandoned cart fires on the highest-intent moment in the customer journey (a known shopper has put items in their basket and left). Median lags of 1 to 4 hours are normal, the customer is often still in the same browsing session when they receive Step 1. If your abandoned-cart programme is not at or near the top, something is broken: pixel mis-firing, programme paused, or a deliverability issue. My one-shot campaigns show longer lags than programmes. Should I move spend toward programmes? Usually no. The lag is a behavioural artefact, not a quality signal. Programmes fire on intent triggers and naturally see short lags; campaigns are blast sends to a broader audience whose intent state varies. The right read is recovered revenue per send, not lag. A 14-hour-lag campaign that drives £24,800 with one send is still more efficient than a programme that drives £4,800 over 4,400 recipients. Can I optimise to shrink the lag on a slow programme? Sometimes yes, sometimes no, depending on the programme intent. Win-back and re-engagement programmes have long lags by design (you’re waking a sleeping audience); shortening the lag would mean sending more aggressively to dormant contacts, which spikes unsubscribes and spam complaints. Welcome and post-purchase programmes have more flexibility; tighter sequencing (Day 1, Day 2, Day 4 instead of Day 1, Day 5, Day 10) often pulls lag down without engagement loss. Why is the median lag on my Mother’s Day campaign so much shorter than my Spring Edit campaign? Tentpole moments compress consideration time. Mother’s Day, Black Friday, Valentine’s, all have a hard date that pulls customers off the fence faster. Spring Edit is a soft-promotional campaign without a deadline; customers browse, save, return days later. The lag pattern is a useful signal that confirms which promotions are actually time-sensitive (consumer perception) vs which are pretending to be. Does the card include orders placed via channels other than the web store? Only orders flowing through the connected commerce platform (BigCommerce, Magento, etc.) and producing aPlaced Order event in Dotdigital. POS orders, Amazon orders, marketplace orders, and manual phone orders that don’t sync back as Dotdigital Placed Order events are not counted. For multi-channel merchants, this card is “web-attributed” lag.
Can a customer’s order be attributed to multiple sends?
No. Last-touch attribution: the most recent click before the order gets full credit. The lag is calculated against that most recent click. This means a customer who clicks Step 1 on Monday, Step 2 on Tuesday, then orders Wednesday morning shows up under Step 2’s lag, not Step 1’s. The aggregate revenue is correct (no double-counting); the per-send breakdown reflects the customer’s most recent interaction.
Why is the lag for my SMS campaigns much shorter than email?
SMS has higher open rates (~95% vs ~25% for email) and higher immediate-action rates because the message is on the customer’s lock screen. SMS lags of 30 minutes to 2 hours are normal; email lags of 6 to 24 hours are normal. If you’re running both channels, the SMS lag will pull the campaign’s median down even when most of the recovered revenue came from email.
What if my top sends are all below the lag-rank threshold (e.g. all programmes show 24+ hour lags)?
That’s a signal worth diagnosing. Either (a) your audience has long consideration cycles (B2B, high-AOV homewares, gifting), or (b) your programmes are firing too late (Step 1 of abandoned cart sent 2 hours after abandon instead of 30 minutes, for example). Check the programme delay settings; modern best practice is Step 1 at 1-4 hours after abandon, Step 2 at 24 hours, Step 3 at 48 to 72 hours.