Tag the link the deal site uses, give vouchers a code you record, and months later compare returning visits and bookings from those customers against your job records before deciding whether to run another deal.

A busy month, then quiet

A daily deal brings a rush. Vouchers sell out in a day, the phone rings for weeks, and the calendar fills with people who have never bought from you before.

That part is easy to see. The question that decides whether it was worth doing comes later: how many of those people came back and paid full price.

Most businesses never find out. The deal ends, the vouchers are redeemed, and by the time anyone asks, a deal customer looks like every other customer.

Analytics can answer part of the question and your own records the rest, but only if the deal was set up to be measured before it ran.

What a deal customer is often buying

The first is the hardest to undo. A customer who paid half price has a reference point, and your full price now looks like a markup on the price they know.

Why the return is the whole case

The deal site keeps a share of an already discounted price, so a business often earns little or nothing on each voucher. The payoff is meant to come later.

Without return visits, a deal is a busy month at a loss. That can still make sense as advertising, but it should be judged as advertising, against what the same money buys elsewhere.

So the decision to run a second deal should rest on what the first one produced, not on how busy it felt.

Tag the link before it goes out

Deal pages usually link to the business's website. Ask the deal company to use a link with campaign tags: short labels added to the address that tell Analytics the source, the medium and the campaign.

Google's URL Builder makes one in a minute. Without tags, visits show up only as a referral from the deal site, with nothing tying them to this deal.

Note the voucher codes too. Add a field for the code to the booking form, and record each code against the customer's name in your job records.

Reading new against returning

Analytics labels each visit new or returning, depending on whether that browser has visited before. Add User Type as a secondary dimension on the deal campaign's report and you see both.

In the weeks after the deal, nearly all deal visits are new. The ones that matter come later: returning visits from the same campaign, and among them, visits to the booking or quote page.

A goal on the booking confirmation page turns those into a figure you can compare month by month. Check at three and six months, or after a full year for seasonal work.

What Analytics cannot see

Returning means the same browser on the same device. A customer who bought the voucher on a phone and comes back on a work computer counts as new, and so does one who cleared their cookies.

Customers who phone instead of booking online never appear at all.

So Analytics shows the direction and your records give the answer. A customer list with voucher codes beside the names shows exactly who came back and what they paid.

A worked example

A ceiling fan installer in a suburb, one electrician and an apprentice, installs and replaces fans, light fixtures and the occasional switch for homeowners.

A year ago the business ran a daily deal: installation of one customer-supplied ceiling fan at a fixed low price. The vouchers sold out over a weekend, and deal jobs filled the next six weeks.

The deal page linked to the home page with no tags. By this summer Analytics could not say whether any of those customers had come back, and another deal company was offering a second run.

Going through the job records by voucher code answered it. A handful had booked more work at full price, mostly a second fan or new lighting; most had not been heard from since.

Before agreeing to anything, the owner set the next deal up to be measured: a tagged link, a voucher code field on the booking form, a goal on the confirmation page, and calendar reminders at three and six months.

The offer changed too. It now includes a look at the home's other fixtures, so each visit ends with a written quote for anything else the customer mentioned.

Deciding whether to run another

Add up what the deal cost: the discount, the deal site's share and the hours spent. Compare it with full-price work that came back within the period you chose.

Count only work that would not have happened anyway. A regular customer who used a voucher for a job they would have booked regardless is a cost, not a gain.

Making a return more likely

Treat deal customers exactly as you treat full-price ones. They are watching for any difference, and they tell friends what they find.

Ask, clearly and in person, whether they would like to hear from you by email, and keep a record of who said yes. An explicit yes is clearer under CASL than relying on the voucher purchase.

Leave something that points to the next visit: a card with the direct booking link, a seasonal reminder, or a written quote for the next job.

The counter-case

Some businesses run a deal for a reason other than return visits: to fill a quiet season or to introduce a new service. Judged by that purpose, a deal can be worthwhile without a single return.

A business selling something people buy once, such as a family portrait session, should expect few returns and judge the deal on referrals instead.

Either way, decide what the deal is for before it runs, tag its link, record its vouchers, and read the results months later rather than during the rush.

Before the next deal

  1. Decide what the deal is for before signing.
  2. Build a tagged link for the deal page.
  3. Add a voucher code field to the booking form.
  4. Set a goal on the booking confirmation page.
  5. Record each voucher against the customer's name.
  6. Check returning visits at three and six months.
  7. Compare full-price work with the deal's cost.

Step five is the one that settles the question. Analytics loses people who switch devices or phone instead, but a customer list with voucher codes beside the names shows exactly who came back.

Checking Analytics against your own records is the habit described in revenue missing from an online shop's analytics.

Frequently asked questions

How do I track a daily deal in Analytics?

Ask the deal company to use a link with campaign tags for source, medium and campaign, then view that campaign's visits with User Type as a secondary dimension.

What does returning visitor mean in Analytics?

A visit from a browser that has been to the site before. A customer who switches devices or clears cookies counts as new, so the figure undercounts returns.

Why do deal customers often not return?

Many bought the price rather than the business, and the deal price became their idea of what you charge. Others bought a one-off job or will follow the next deal.

When should I check the results?

At three and six months, and at a year for seasonal work. In the first weeks nearly every deal visit is new, which says nothing about returns.

What is the most reliable measure?

Your own records. Note each voucher code against the customer's name, then see who booked full-price work later and what it was worth.

How do I decide on another deal?

Compare the deal's full cost, including the discount, the deal site's share and the hours, with full-price work that would not otherwise have happened.

West Coast Media Solutions Inc. provides web design, web development, hosting, digital marketing, and business consulting to organisations across Canada, drawing on more than twenty-five years in the field.

Ran a daily deal and never measured it?

Tell us when it ran and what it sold. We will set up the tags, voucher field and goal before the next.

Start a Conversation