MT358 Unit 9 Social Media Analytics Report Example

What the Studio Would Miss: An MT358 Unit 9 Social Analytics Report on a Yarn Studio's Summer Quarter

[Student Name]

Purdue University Global

MT358: Social Media Marketing and AI Optimization

Unit 9 Social Analytics Report

[Instructor Name]

[Date]

The studio, sources and figures are composite; the measurement research cited is real.

Summary for the Owners

The question this report answers is simple: which social activity would the studio miss if it stopped tomorrow? Three things, in order. The Ravelry group, which brought about a fifth of social revenue from less than a tenth of social visits. Pattern-pairing posts, which averaged 11.2 traced orders each. And the summer knit-along, whose new buyers came back at four times the rate of buyers who arrived from reels. Trend reels are the clearest candidate to drop. Paid social, judged on tracked purchases rather than the platform's count, cost $51.06 per purchase, above the $45.24 break-even.

How the Figures Were Gathered

Every figure in the source tables comes from the studio's own records: tagged links on every post and profile, a discount code unique to each source and the store's order system. Platform dashboards supply only the reach and follower figures in the appendix. In a study of an Indian ice cream chain, Kumar et al. (2013) demonstrated that social media can be measured by the sales and customer value it produces rather than by likes and shares, provided each activity is traced to purchases. This report follows that approach, and every source is shown with the same four measures: sessions, orders, revenue and the 90-day reorder rate among each source's first-time buyers.

Five Sources, One Set of Measures

Social activity brought 25,300 sessions to the store during the quarter and $50,490 in revenue.

Instagram organic posts brought 14,219 sessions, 56.2 percent of the social total, and 321 orders worth $24,740, or 49.0 percent of social revenue. Its conversion rate was 2.26 percent. It is the studio's largest source and will remain so, but its share of revenue is smaller than its share of visits.

The Ravelry group brought 2,100 sessions, only 8.3 percent of the total, and 121 orders worth $10,300, 20.4 percent of social revenue. It converted at 5.76 percent, more than double Instagram, and its average order of about $85 was the highest of any source.

Paid social brought 7,600 tracked sessions and 141 tracked purchases worth $10,998. It is ranked below Ravelry despite more revenue, because it cost $7,200 to produce and its buyers reordered less often.

YouTube brought 1,000 sessions and 45 orders worth $3,600, a 4.5 percent conversion rate from a small audience that watches full dye-day videos.

The Facebook page brought 381 sessions and 12 orders worth $852. It is reported for completeness and is not a priority.

Small Traffic, Large Revenue

The Ravelry figures are the report's most important finding. A source that produced 8.3 percent of sessions produced 20.4 percent of revenue, and it did so without any paid spending. The explanation is intent. Members of the group arrive already asking a specific question, usually whether a colorway or skein count suits a pattern they have chosen. They are not browsing. They are planning a project, and a project means several skeins. Ranked by sessions, Ravelry is third of five; ranked by revenue and repeat buying, it sits second among the organic sources, behind only Instagram, and first of all five on revenue per visit.

The group that sends the least traffic sends the knitters who have already decided what to make.

Reach Against Orders

Post types are compared separately from platforms, because the studio's staff time is spent by type: an hour on a reel is an hour not spent on a pattern pairing. During the quarter, the studio posted nine pattern pairings and ten trend reels on Instagram. Trend reels averaged 19,400 accounts reached and 0.6 traced orders per post, six orders in total. Pattern pairings averaged 6,100 reached and 11.2 traced orders per post, about 101 orders in total. Saves tell the same story: pairings drew 67.2 saves per thousand accounts reached, reels 4.9. A save usually means a knitter intends to return to a pattern, which is why saves track orders more closely than reach does.

Each reel took about three hours to plan, film and edit; each pairing took about an hour and a half. The reels consumed roughly 30 staff hours for six orders. The report's main recommendation is therefore a reallocation of hours, not of money: stop the trend reels and move that time to pattern pairings and to answering questions in the Ravelry group.

Customers Who Come Back

Revenue in one quarter understates the value of a customer who returns. Fader et al. (2005) showed that how recently and how often customers buy predicts their future value better than any single purchase does, which is why this report tracks reordering by source. Of 84 first-time buyers who arrived through the summer knit-along, 32 reordered within 90 days, a rate of 38.1 percent. Of 67 first-time buyers who arrived from reels, six reordered, 9.0 percent. By this measure, a knit-along buyer is roughly four times likelier than a reel buyer to turn into a regular. Among the 112 first-time buyers who arrived through paid social, 16 reordered, a rate of 14.3 percent.

196 Reported, 141 Tracked

The ad platform reported 196 purchases from the quarter's $7,200 in paid social, a cost of $36.73 each. Tagged links and codes traced 141 purchases, a cost of $51.06 each. The gap has three main causes. First, the platform counts view-through purchases, made by people who saw an ad without clicking and bought within a day; some of those would have bought anyway. Second, the platform models purchases it cannot observe directly and adds them to its count. Third, tagged links miss some real purchases, for example when a knitter taps an ad on a phone and buys later on a laptop. Li and Kannan (2014) found that common attribution rules give channels credit for conversions that would have happened through other routes, and the platform's count is a version of that problem.

The report judges paid social on the tracked number while noting that it undercounts somewhat. On that basis, the $7,200 earned a gross margin of about $6,379 on first orders, short of its cost. Adding the margin on the 16 reorders brings the total to about $7,103, still $97 short. Paid social roughly broke even only when repeat buying was counted, and lost money on the platform's preferred view. Gordon et al. (2019) found that observational estimates of ad effects can differ widely from experimental ones, so neither figure is the true effect; a holdout test would be needed to settle it.

Recommendations

Stop the trend reels and move their hours to pattern pairings and the Ravelry group. Run the next knit-along, since its buyers return at the highest rate. Keep paid social on trial at its current budget only while tracked cost per knitter purchase stays near break-even, and ask whether the platform can run a holdout test. Report all sources each quarter with the same four measures.

Limits

Codes and tags cannot capture word of mouth, purchases in the Duluth shop that carries the studio's yarn or buyers who heard about a colorway in the Ravelry group and later searched for the studio by name. The 90-day reorder figure covers only first-time buyers who arrived in July, since later buyers have not yet had 90 days. Six reel orders and eleven pairing orders per post are small numbers, and one unusually popular pairing could move an average.

Appendix: Reach and Followers

Instagram followers grew by 3,900 during the quarter. Reach by post type and platform is listed in the appendix tables. These figures are reported but not interpreted, because none of the recommendations depends on them.

References

Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. https://doi.org/10.1509/jmkr.2005.42.4.415

Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193-225. https://doi.org/10.1287/mksc.2018.1135

Kumar, V., Bhaskaran, V., Mirchandani, R., & Shah, M. (2013). Creating a measurable social media marketing strategy: Increasing the value and ROI of intangibles and tangibles for Hokey Pokey. Marketing Science, 32(2), 194-212. https://doi.org/10.1287/mksc.1120.0768

Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40-56. https://doi.org/10.1509/jmr.13.0050